Instruction stringlengths 362 7.83k | output_code stringlengths 1 945 |
|---|---|
Here is a snippet: <|code_start|>
path_new = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol'])
# 这里不应当出错，因为之前已经导出过数据到
df_new = pd.read_hdf(path_new)
if df_new is None:
return None
df_new = filter_dataframe(df_new, 'DateTime', None, None, None)
path_old = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
try:
# 没有以前的数据
df_old = pd.read_hdf(path_old)
if df_old is None:
df = df_new
else:
df_old = filter_dataframe(df_old, 'DateTime', None, None, None)
# 数据合并，不能简单的合并
# 需要保留老的，新的重复的地方忽略
last_ts = df_old.index[-1]
df_new2 = df_new[last_ts:][1:]
df = pd.concat([df_old, df_new2])
except:
df = df_new
# 有可能没有除权文件
div_path = os.path.join(__CONFIG_H5_STK_DIVIDEND_DIR__, "%s.h5" % t['local_symbol'])
try:
div = pd.read_hdf(div_path)
div = filter_dataframe(div, 'time', None, None, None)
<|code_end|>
. Write the next line using the current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_DIVIDEND_DIR__
from kquant_data.stock.stock import merge_adjust_factor, bars_to_h5
from kquant_data.processing.utils import filter_dataframe, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
, which may include functions, classes, or code. Output only the next line. | df = merge_adjust_factor(df, div) |
Predict the next line after this snippet: <|code_start|> df_new = filter_dataframe(df_new, 'DateTime', None, None, None)
path_old = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
try:
# 没有以前的数据
df_old = pd.read_hdf(path_old)
if df_old is None:
df = df_new
else:
df_old = filter_dataframe(df_old, 'DateTime', None, None, None)
# 数据合并，不能简单的合并
# 需要保留老的，新的重复的地方忽略
last_ts = df_old.index[-1]
df_new2 = df_new[last_ts:][1:]
df = pd.concat([df_old, df_new2])
except:
df = df_new
# 有可能没有除权文件
div_path = os.path.join(__CONFIG_H5_STK_DIVIDEND_DIR__, "%s.h5" % t['local_symbol'])
try:
div = pd.read_hdf(div_path)
div = filter_dataframe(div, 'time', None, None, None)
df = merge_adjust_factor(df, div)
except:
# 这里一般是文件没找到，表示没有除权信息
df['backward_factor'] = 1
df['forward_factor'] = 1
<|code_end|>
using the current file's imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_DIVIDEND_DIR__
from kquant_data.stock.stock import merge_adjust_factor, bars_to_h5
from kquant_data.processing.utils import filter_dataframe, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | bars_to_h5(path_old, df) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
将新生成的5分钟数据与老的5分钟数据进行合并
合并出来的数据只用于生成5分钟的单文件数据时使用，其它情况下不使用
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
path_new = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol'])
# 这里不应当出错，因为之前已经导出过数据到
df_new = pd.read_hdf(path_new)
if df_new is None:
return None
<|code_end|>
, predict the next line using imports from the current file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_DIVIDEND_DIR__
from kquant_data.stock.stock import merge_adjust_factor, bars_to_h5
from kquant_data.processing.utils import filter_dataframe, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | df_new = filter_dataframe(df_new, 'DateTime', None, None, None) |
Using the snippet: <|code_start|> df_old = filter_dataframe(df_old, 'DateTime', None, None, None)
# 数据合并，不能简单的合并
# 需要保留老的，新的重复的地方忽略
last_ts = df_old.index[-1]
df_new2 = df_new[last_ts:][1:]
df = pd.concat([df_old, df_new2])
except:
df = df_new
# 有可能没有除权文件
div_path = os.path.join(__CONFIG_H5_STK_DIVIDEND_DIR__, "%s.h5" % t['local_symbol'])
try:
div = pd.read_hdf(div_path)
div = filter_dataframe(div, 'time', None, None, None)
df = merge_adjust_factor(df, div)
except:
# 这里一般是文件没找到，表示没有除权信息
df['backward_factor'] = 1
df['forward_factor'] = 1
bars_to_h5(path_old, df)
if __name__ == '__main__':
# 此合并h5的代码已经废弃不用
_input = '5min_lc5'
_ouput = '5min'
instruments = get_folder_symbols(__CONFIG_H5_STK_DIR__, _input)
<|code_end|>
, determine the next line of code. You have imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_DIVIDEND_DIR__
from kquant_data.stock.stock import merge_adjust_factor, bars_to_h5
from kquant_data.processing.utils import filter_dataframe, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | multiprocessing_convert(True, '5min', _input, _ouput, instruments, _export_data) |
Given the following code snippet before the placeholder: <|code_start|> df = df_new
else:
df_old = filter_dataframe(df_old, 'DateTime', None, None, None)
# 数据合并，不能简单的合并
# 需要保留老的，新的重复的地方忽略
last_ts = df_old.index[-1]
df_new2 = df_new[last_ts:][1:]
df = pd.concat([df_old, df_new2])
except:
df = df_new
# 有可能没有除权文件
div_path = os.path.join(__CONFIG_H5_STK_DIVIDEND_DIR__, "%s.h5" % t['local_symbol'])
try:
div = pd.read_hdf(div_path)
div = filter_dataframe(div, 'time', None, None, None)
df = merge_adjust_factor(df, div)
except:
# 这里一般是文件没找到，表示没有除权信息
df['backward_factor'] = 1
df['forward_factor'] = 1
bars_to_h5(path_old, df)
if __name__ == '__main__':
# 此合并h5的代码已经废弃不用
_input = '5min_lc5'
_ouput = '5min'
<|code_end|>
, predict the next line using imports from the current file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_DIVIDEND_DIR__
from kquant_data.stock.stock import merge_adjust_factor, bars_to_h5
from kquant_data.processing.utils import filter_dataframe, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | instruments = get_folder_symbols(__CONFIG_H5_STK_DIR__, _input) |
Predict the next line for this snippet: <|code_start|> sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
path = r'D:\DATA_FUT\sectorconstituent\CHN_FUT\19950417.csv'
path = r'D:\DATA_FUT\sectorconstituent\CHN_FUT\\'
for dirpath, dirnames, filenames in os.walk(path):
for filename in filenames:
# print(filename)
new_filename = "%s-%s-%s.csv" % (filename[0:4], filename[4:6], filename[6:8])
print(new_filename)
new_path = os.path.join(dirpath, filename)
df = pd.read_csv(new_path, encoding='gbk', parse_dates=True)
# 期权可能名字也很重要，但如果在多个文件中都出现这个名字又太麻烦，最好还是使用别的表来映射名字
# df = df[['wind_code', 'sec_name']]
df = df[['wind_code']]
df_SHF = df.loc[df['wind_code'].str.endswith('.SHF')]
df_CFE = df.loc[df['wind_code'].str.endswith('.CFE')]
df_DCE = df.loc[df['wind_code'].str.endswith('.DCE')]
df_CZC = df.loc[df['wind_code'].str.endswith('.CZC')]
path_SHF = r'D:\DATA_FUT\sectorconstituent\上期所全部品种\%s' % new_filename
path_CFE = r'D:\DATA_FUT\sectorconstituent\中金所全部品种\%s' % new_filename
path_DCE = r'D:\DATA_FUT\sectorconstituent\大商所全部品种\%s' % new_filename
path_CZC = r'D:\DATA_FUT\sectorconstituent\郑商所全部品种\%s' % new_filename
if len(df_SHF) > 0:
<|code_end|>
with the help of current file imports:
import sys
import pandas as pd
import os
from WindPy import w
from kquant_data.wind.wset import write_constituent
and context from other files:
# Path: kquant_data/wind/wset.py
# def write_constituent(path, df):
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=False)
, which may contain function names, class names, or code. Output only the next line. | write_constituent(path_SHF, df_SHF) |
Given the code snippet: <|code_start|>print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
def process_dir2file(w, mydir, myfile):
df = read_data_dataframe(myfile)
all_set = set(df.index)
for dirpath, dirnames, filenames in os.walk(mydir):
for filename in filenames:
# 这个日期需要记得修改
if filename < "2017-01-01.csv":
continue
filepath = os.path.join(dirpath, filename)
df1 = read_constituent(filepath)
# print(filepath)
if df1 is None:
continue
if df1.empty:
continue
curr_set = set(df1['wind_code'])
diff_set = curr_set - all_set
if len(diff_set) == 0:
continue
print(filepath)
<|code_end|>
, generate the next line using the imports in this file:
from WindPy import w
from kquant_data.wind.wss import download_ipo_last_trade_trading
from kquant_data.xio.csv import write_data_dataframe, read_data_dataframe
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import read_constituent
import sys
import os
import pandas as pd
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/wind/wss.py
# def download_ipo_last_trade_trading(w, wind_codes):
# # 黄金从20130625开始将最小变动价位从0.01调整成了0.05，但从万得上查出来还是完全一样，所以没有必要记录mfprice
# # 郑商所在修改合约交易单位时都改了合约代码，所以没有必要记录contractmultiplier
# w.asDateTime = asDateTime
# w_wss_data = w.wss(wind_codes, "sec_name,ipo_date,lasttrade_date,lasttradingdate", "")
# grid = w_wss_data.Data
#
# # T1803一类的会被当成时间，需要提前转置
# new_grid = [[row[i] for row in grid] for i in range(len(grid[0]))]
#
# df = pd.DataFrame(new_grid)
# df.columns = ['sec_name', 'ipo_date', 'lasttrade_date', 'lasttradingdate']
# df.index = w_wss_data.Codes
# df.index.name = 'wind_code'
#
# df['ipo_date'] = df['ipo_date'].apply(datetime_2_yyyyMMdd)
# df['lasttrade_date'] = df['lasttrade_date'].apply(datetime_2_yyyyMMdd)
# df['lasttradingdate'] = df['lasttradingdate'].apply(datetime_2_yyyyMMdd)
#
# df.replace(18991230, 0, inplace=True)
#
# return df
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | df2 = download_ipo_last_trade_trading(w, list(diff_set)) |
Given the following code snippet before the placeholder: <|code_start|>except:
pass
def process_dir2file(w, mydir, myfile):
df = read_data_dataframe(myfile)
all_set = set(df.index)
for dirpath, dirnames, filenames in os.walk(mydir):
for filename in filenames:
# 这个日期需要记得修改
if filename < "2017-01-01.csv":
continue
filepath = os.path.join(dirpath, filename)
df1 = read_constituent(filepath)
# print(filepath)
if df1 is None:
continue
if df1.empty:
continue
curr_set = set(df1['wind_code'])
diff_set = curr_set - all_set
if len(diff_set) == 0:
continue
print(filepath)
df2 = download_ipo_last_trade_trading(w, list(diff_set))
df = pd.concat([df, df2])
all_set = set(df.index)
# 出于安全考虑，还是每次都保存
<|code_end|>
, predict the next line using imports from the current file:
from WindPy import w
from kquant_data.wind.wss import download_ipo_last_trade_trading
from kquant_data.xio.csv import write_data_dataframe, read_data_dataframe
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import read_constituent
import sys
import os
import pandas as pd
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/wind/wss.py
# def download_ipo_last_trade_trading(w, wind_codes):
# # 黄金从20130625开始将最小变动价位从0.01调整成了0.05，但从万得上查出来还是完全一样，所以没有必要记录mfprice
# # 郑商所在修改合约交易单位时都改了合约代码，所以没有必要记录contractmultiplier
# w.asDateTime = asDateTime
# w_wss_data = w.wss(wind_codes, "sec_name,ipo_date,lasttrade_date,lasttradingdate", "")
# grid = w_wss_data.Data
#
# # T1803一类的会被当成时间，需要提前转置
# new_grid = [[row[i] for row in grid] for i in range(len(grid[0]))]
#
# df = pd.DataFrame(new_grid)
# df.columns = ['sec_name', 'ipo_date', 'lasttrade_date', 'lasttradingdate']
# df.index = w_wss_data.Codes
# df.index.name = 'wind_code'
#
# df['ipo_date'] = df['ipo_date'].apply(datetime_2_yyyyMMdd)
# df['lasttrade_date'] = df['lasttrade_date'].apply(datetime_2_yyyyMMdd)
# df['lasttradingdate'] = df['lasttradingdate'].apply(datetime_2_yyyyMMdd)
#
# df.replace(18991230, 0, inplace=True)
#
# return df
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | write_data_dataframe(myfile, df) |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载合约信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
def process_dir2file(w, mydir, myfile):
<|code_end|>
, continue by predicting the next line. Consider current file imports:
from WindPy import w
from kquant_data.wind.wss import download_ipo_last_trade_trading
from kquant_data.xio.csv import write_data_dataframe, read_data_dataframe
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import read_constituent
import sys
import os
import pandas as pd
and context:
# Path: kquant_data/wind/wss.py
# def download_ipo_last_trade_trading(w, wind_codes):
# # 黄金从20130625开始将最小变动价位从0.01调整成了0.05，但从万得上查出来还是完全一样，所以没有必要记录mfprice
# # 郑商所在修改合约交易单位时都改了合约代码，所以没有必要记录contractmultiplier
# w.asDateTime = asDateTime
# w_wss_data = w.wss(wind_codes, "sec_name,ipo_date,lasttrade_date,lasttradingdate", "")
# grid = w_wss_data.Data
#
# # T1803一类的会被当成时间，需要提前转置
# new_grid = [[row[i] for row in grid] for i in range(len(grid[0]))]
#
# df = pd.DataFrame(new_grid)
# df.columns = ['sec_name', 'ipo_date', 'lasttrade_date', 'lasttradingdate']
# df.index = w_wss_data.Codes
# df.index.name = 'wind_code'
#
# df['ipo_date'] = df['ipo_date'].apply(datetime_2_yyyyMMdd)
# df['lasttrade_date'] = df['lasttrade_date'].apply(datetime_2_yyyyMMdd)
# df['lasttradingdate'] = df['lasttradingdate'].apply(datetime_2_yyyyMMdd)
#
# df.replace(18991230, 0, inplace=True)
#
# return df
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
which might include code, classes, or functions. Output only the next line. | df = read_data_dataframe(myfile) |
Predict the next line after this snippet: <|code_start|> filepath = os.path.join(dirpath, filename)
df1 = read_constituent(filepath)
# print(filepath)
if df1 is None:
continue
if df1.empty:
continue
curr_set = set(df1['wind_code'])
diff_set = curr_set - all_set
if len(diff_set) == 0:
continue
print(filepath)
df2 = download_ipo_last_trade_trading(w, list(diff_set))
df = pd.concat([df, df2])
all_set = set(df.index)
# 出于安全考虑，还是每次都保存
write_data_dataframe(myfile, df)
df['wind_code'] = df.index
df.sort_values(by=['ipo_date', 'wind_code'], inplace=True)
del df['wind_code']
write_data_dataframe(myfile, df)
if __name__ == '__main__':
w.start()
# 先读取数据，合并，找不同，然后下单
<|code_end|>
using the current file's imports:
from WindPy import w
from kquant_data.wind.wss import download_ipo_last_trade_trading
from kquant_data.xio.csv import write_data_dataframe, read_data_dataframe
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import read_constituent
import sys
import os
import pandas as pd
and any relevant context from other files:
# Path: kquant_data/wind/wss.py
# def download_ipo_last_trade_trading(w, wind_codes):
# # 黄金从20130625开始将最小变动价位从0.01调整成了0.05，但从万得上查出来还是完全一样，所以没有必要记录mfprice
# # 郑商所在修改合约交易单位时都改了合约代码，所以没有必要记录contractmultiplier
# w.asDateTime = asDateTime
# w_wss_data = w.wss(wind_codes, "sec_name,ipo_date,lasttrade_date,lasttradingdate", "")
# grid = w_wss_data.Data
#
# # T1803一类的会被当成时间，需要提前转置
# new_grid = [[row[i] for row in grid] for i in range(len(grid[0]))]
#
# df = pd.DataFrame(new_grid)
# df.columns = ['sec_name', 'ipo_date', 'lasttrade_date', 'lasttradingdate']
# df.index = w_wss_data.Codes
# df.index.name = 'wind_code'
#
# df['ipo_date'] = df['ipo_date'].apply(datetime_2_yyyyMMdd)
# df['lasttrade_date'] = df['lasttrade_date'].apply(datetime_2_yyyyMMdd)
# df['lasttradingdate'] = df['lasttradingdate'].apply(datetime_2_yyyyMMdd)
#
# df.replace(18991230, 0, inplace=True)
#
# return df
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | outputFile = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, 'ipo_last_trade_trading.csv') |
Given the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载合约信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
def process_dir2file(w, mydir, myfile):
df = read_data_dataframe(myfile)
all_set = set(df.index)
for dirpath, dirnames, filenames in os.walk(mydir):
for filename in filenames:
# 这个日期需要记得修改
if filename < "2017-01-01.csv":
continue
filepath = os.path.join(dirpath, filename)
<|code_end|>
, generate the next line using the imports in this file:
from WindPy import w
from kquant_data.wind.wss import download_ipo_last_trade_trading
from kquant_data.xio.csv import write_data_dataframe, read_data_dataframe
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import read_constituent
import sys
import os
import pandas as pd
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/wind/wss.py
# def download_ipo_last_trade_trading(w, wind_codes):
# # 黄金从20130625开始将最小变动价位从0.01调整成了0.05，但从万得上查出来还是完全一样，所以没有必要记录mfprice
# # 郑商所在修改合约交易单位时都改了合约代码，所以没有必要记录contractmultiplier
# w.asDateTime = asDateTime
# w_wss_data = w.wss(wind_codes, "sec_name,ipo_date,lasttrade_date,lasttradingdate", "")
# grid = w_wss_data.Data
#
# # T1803一类的会被当成时间，需要提前转置
# new_grid = [[row[i] for row in grid] for i in range(len(grid[0]))]
#
# df = pd.DataFrame(new_grid)
# df.columns = ['sec_name', 'ipo_date', 'lasttrade_date', 'lasttradingdate']
# df.index = w_wss_data.Codes
# df.index.name = 'wind_code'
#
# df['ipo_date'] = df['ipo_date'].apply(datetime_2_yyyyMMdd)
# df['lasttrade_date'] = df['lasttrade_date'].apply(datetime_2_yyyyMMdd)
# df['lasttradingdate'] = df['lasttradingdate'].apply(datetime_2_yyyyMMdd)
#
# df.replace(18991230, 0, inplace=True)
#
# return df
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | df1 = read_constituent(filepath) |
Given snippet: <|code_start|> rolling_count.count += 1
else:
rolling_count.previous = val
rolling_count.count = 0
return rolling_count.count
rolling_count.count = 0 # static variable
rolling_count.previous = None # static variable
def series_drop_duplicated_keep_both_rolling(series):
"""
删除重复，只保留前后两端的数据
如果中间出现重复数据也能使用了
:param series:
:return:
"""
rolling_count.previous = None
_count_ = series.apply(rolling_count)
_first_ = _count_ == 0
_last_ = _first_.shift(-1)
_last_[-1] = True
series = series[_first_ | _last_]
return series
def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
if index_name is not None:
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import os
import datetime
import numpy as np
import pandas as pd
import multiprocessing
from functools import partial
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime, datetime_2_yyyyMMddHHmm, tic, toc
from ..xio.csv import read_data_dataframe
and context:
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# def datetime_2_yyyyMMddHHmm(dt):
# """
# 将时间转换成float类型
# :param dt:
# :return:
# """
# t = dt.timetuple()
# return float((t.tm_year * 10000.0 + t.tm_mon * 100 + t.tm_mday) * 10000.0) + t.tm_hour * 100 + t.tm_min
#
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
which might include code, classes, or functions. Output only the next line. | df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime) |
Given the following code snippet before the placeholder: <|code_start|> # 'Time': 'first',
# 'BarSize': 'first',
# 'Pad': 'min',
# 'Open': 'first',
# 'High': 'max',
# 'Low': 'min',
# 'Close': 'last',
# 'Volume': 'sum',
# 'Amount': 'sum',
# 'OpenInterest': 'last',
# 'Settle': 'last',
# 'AdjustFactorPM': 'last',
# 'AdjustFactorTD': 'last',
# 'BAdjustFactorPM': 'last',
# 'BAdjustFactorTD': 'last',
# 'FAdjustFactorPM': 'last',
# 'FAdjustFactorTD': 'last',
# 'MoneyFlow': 'sum',
# }
columns = df.columns
new = df.resample(rule, closed='left', label='left').apply(how_dict)
new.dropna(inplace=True)
# 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
new = new[new['Open'] != 0]
# 居然位置要调整一下
new = new[columns]
# 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
<|code_end|>
, predict the next line using imports from the current file:
import os
import datetime
import numpy as np
import pandas as pd
import multiprocessing
from functools import partial
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime, datetime_2_yyyyMMddHHmm, tic, toc
from ..xio.csv import read_data_dataframe
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# def datetime_2_yyyyMMddHHmm(dt):
# """
# 将时间转换成float类型
# :param dt:
# :return:
# """
# t = dt.timetuple()
# return float((t.tm_year * 10000.0 + t.tm_mon * 100 + t.tm_mday) * 10000.0) + t.tm_hour * 100 + t.tm_min
#
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
. Output only the next line. | new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm) |
Using the snippet: <|code_start|> # 'Close': 'last',
# 'Volume': 'sum',
# 'Amount': 'sum',
# 'OpenInterest': 'last',
# 'Settle': 'last',
# 'AdjustFactorPM': 'last',
# 'AdjustFactorTD': 'last',
# 'BAdjustFactorPM': 'last',
# 'BAdjustFactorTD': 'last',
# 'FAdjustFactorPM': 'last',
# 'FAdjustFactorTD': 'last',
# 'MoneyFlow': 'sum',
# }
columns = df.columns
new = df.resample(rule, closed='left', label='left').apply(how_dict)
new.dropna(inplace=True)
# 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
new = new[new['Open'] != 0]
# 居然位置要调整一下
new = new[columns]
# 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
return new
def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
<|code_end|>
, determine the next line of code. You have imports:
import os
import datetime
import numpy as np
import pandas as pd
import multiprocessing
from functools import partial
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime, datetime_2_yyyyMMddHHmm, tic, toc
from ..xio.csv import read_data_dataframe
and context (class names, function names, or code) available:
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# def datetime_2_yyyyMMddHHmm(dt):
# """
# 将时间转换成float类型
# :param dt:
# :return:
# """
# t = dt.timetuple()
# return float((t.tm_year * 10000.0 + t.tm_mon * 100 + t.tm_mday) * 10000.0) + t.tm_hour * 100 + t.tm_min
#
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
. Output only the next line. | tic() |
Here is a snippet: <|code_start|> # }
columns = df.columns
new = df.resample(rule, closed='left', label='left').apply(how_dict)
new.dropna(inplace=True)
# 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
new = new[new['Open'] != 0]
# 居然位置要调整一下
new = new[columns]
# 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
return new
def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
tic()
if multi:
pool_size = multiprocessing.cpu_count() - 1
pool = multiprocessing.Pool(processes=pool_size)
func = partial(func_convert, rule, _input, output, instruments)
pool_outputs = pool.map(func, range(len(instruments)))
print('Pool:', pool_outputs)
else:
for i in range(len(instruments)):
func_convert(rule, _input, output, instruments, i)
<|code_end|>
. Write the next line using the current file imports:
import os
import datetime
import numpy as np
import pandas as pd
import multiprocessing
from functools import partial
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime, datetime_2_yyyyMMddHHmm, tic, toc
from ..xio.csv import read_data_dataframe
and context from other files:
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# def datetime_2_yyyyMMddHHmm(dt):
# """
# 将时间转换成float类型
# :param dt:
# :return:
# """
# t = dt.timetuple()
# return float((t.tm_year * 10000.0 + t.tm_mon * 100 + t.tm_mday) * 10000.0) + t.tm_hour * 100 + t.tm_min
#
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
, which may include functions, classes, or code. Output only the next line. | toc() |
Predict the next line after this snippet: <|code_start|> toc()
def dataframe_align_copy(df1, df2):
"""
两个DataFrame，将其中的数据复制到另一个
:param df1:
:param df2:
:return:
"""
index = df1.index.intersection(df2.index)
columns = df1.columns.intersection(df2.columns)
# 由于两边的数据不配套，所以只能复制重合部分
df1.ix[index, columns] = df2.ix[index, columns]
return df1
def read_fill_from_file(path, date, field, df):
"""
将一个文件中的内容合并到一个df中
:param path:
:param date:
:param field:
:param df:
:return:
"""
_path = path % date
<|code_end|>
using the current file's imports:
import os
import datetime
import numpy as np
import pandas as pd
import multiprocessing
from functools import partial
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime, datetime_2_yyyyMMddHHmm, tic, toc
from ..xio.csv import read_data_dataframe
and any relevant context from other files:
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# def datetime_2_yyyyMMddHHmm(dt):
# """
# 将时间转换成float类型
# :param dt:
# :return:
# """
# t = dt.timetuple()
# return float((t.tm_year * 10000.0 + t.tm_mon * 100 + t.tm_mday) * 10000.0) + t.tm_hour * 100 + t.tm_min
#
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
. Output only the next line. | x = read_data_dataframe(_path) |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
已经下载到了每个合约的最新主力信息
现在对数据进一步整理成表单
"""
if __name__ == '__main__':
<|code_end|>
, determine the next line of code. You have imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_FACTOR_DIR__
from kquant_data.xio.csv import read_data_dataframe, write_data_dataframe
from kquant_data.future.symbol import wind_code_2_InstrumentID
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_FACTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'factor')
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/future/symbol.py
# def wind_code_2_InstrumentID(df, field):
# sym_ex = df[field].str.split('.')
# sym_ex = list(sym_ex)
# sym_ex_df = pd.DataFrame(sym_ex, index=df.index)
# sym_ex_df.columns = ['InstrumentID', 'exchange']
# df = pd.concat([df, sym_ex_df], axis=1)
# df['lower'] = (df['exchange'] == 'SHF') | (df['exchange'] == 'DCE') | (df['exchange'] == 'INE')
# df['InstrumentID'][df['lower']] = df['InstrumentID'].str.lower()
# return df
. Output only the next line. | input_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode') |
Based on the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
已经下载到了每个合约的最新主力信息
现在对数据进一步整理成表单
"""
if __name__ == '__main__':
input_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode')
output_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode.csv')
df_csv = pd.DataFrame(columns=['trade_hiscode'])
for dirpath, dirnames, filenames in os.walk(input_path):
for filename in filenames:
shotname, extension = os.path.splitext(filename)
dirpath_filename = os.path.join(dirpath, filename)
print(dirpath_filename)
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_FACTOR_DIR__
from kquant_data.xio.csv import read_data_dataframe, write_data_dataframe
from kquant_data.future.symbol import wind_code_2_InstrumentID
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_FACTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'factor')
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/future/symbol.py
# def wind_code_2_InstrumentID(df, field):
# sym_ex = df[field].str.split('.')
# sym_ex = list(sym_ex)
# sym_ex_df = pd.DataFrame(sym_ex, index=df.index)
# sym_ex_df.columns = ['InstrumentID', 'exchange']
# df = pd.concat([df, sym_ex_df], axis=1)
# df['lower'] = (df['exchange'] == 'SHF') | (df['exchange'] == 'DCE') | (df['exchange'] == 'INE')
# df['InstrumentID'][df['lower']] = df['InstrumentID'].str.lower()
# return df
. Output only the next line. | _df = read_data_dataframe(dirpath_filename) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
已经下载到了每个合约的最新主力信息
现在对数据进一步整理成表单
"""
if __name__ == '__main__':
input_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode')
output_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode.csv')
df_csv = pd.DataFrame(columns=['trade_hiscode'])
for dirpath, dirnames, filenames in os.walk(input_path):
for filename in filenames:
shotname, extension = os.path.splitext(filename)
dirpath_filename = os.path.join(dirpath, filename)
print(dirpath_filename)
_df = read_data_dataframe(dirpath_filename)
_df.dropna(inplace=True)
df_csv.loc[shotname] = None
df_csv.loc[shotname]['trade_hiscode'] = _df.iat[-1, 0]
df = wind_code_2_InstrumentID(df_csv, 'trade_hiscode')
df.index.name = 'product'
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_FACTOR_DIR__
from kquant_data.xio.csv import read_data_dataframe, write_data_dataframe
from kquant_data.future.symbol import wind_code_2_InstrumentID
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_FACTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'factor')
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/future/symbol.py
# def wind_code_2_InstrumentID(df, field):
# sym_ex = df[field].str.split('.')
# sym_ex = list(sym_ex)
# sym_ex_df = pd.DataFrame(sym_ex, index=df.index)
# sym_ex_df.columns = ['InstrumentID', 'exchange']
# df = pd.concat([df, sym_ex_df], axis=1)
# df['lower'] = (df['exchange'] == 'SHF') | (df['exchange'] == 'DCE') | (df['exchange'] == 'INE')
# df['InstrumentID'][df['lower']] = df['InstrumentID'].str.lower()
# return df
, which may contain function names, class names, or code. Output only the next line. | write_data_dataframe(output_path, df) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
已经下载到了每个合约的最新主力信息
现在对数据进一步整理成表单
"""
if __name__ == '__main__':
input_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode')
output_path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'trade_hiscode.csv')
df_csv = pd.DataFrame(columns=['trade_hiscode'])
for dirpath, dirnames, filenames in os.walk(input_path):
for filename in filenames:
shotname, extension = os.path.splitext(filename)
dirpath_filename = os.path.join(dirpath, filename)
print(dirpath_filename)
_df = read_data_dataframe(dirpath_filename)
_df.dropna(inplace=True)
df_csv.loc[shotname] = None
df_csv.loc[shotname]['trade_hiscode'] = _df.iat[-1, 0]
<|code_end|>
. Use current file imports:
(import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_FACTOR_DIR__
from kquant_data.xio.csv import read_data_dataframe, write_data_dataframe
from kquant_data.future.symbol import wind_code_2_InstrumentID)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_FACTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'factor')
#
# Path: kquant_data/xio/csv.py
# def read_data_dataframe(path, sep=','):
# """
# 读取季报的公告日
# 注意：有些股票多个季度一起发，一般是公司出问题了，特别是600878，四个报告同一天发布
# 年报与一季报很有可能一起发
# :param path:
# :param sep:
# :return:
# """
# try:
# df = pd.read_csv(path, index_col=0, parse_dates=True, encoding='utf-8-sig', sep=sep)
# except (FileNotFoundError, OSError):
# return None
#
# return df
#
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/future/symbol.py
# def wind_code_2_InstrumentID(df, field):
# sym_ex = df[field].str.split('.')
# sym_ex = list(sym_ex)
# sym_ex_df = pd.DataFrame(sym_ex, index=df.index)
# sym_ex_df.columns = ['InstrumentID', 'exchange']
# df = pd.concat([df, sym_ex_df], axis=1)
# df['lower'] = (df['exchange'] == 'SHF') | (df['exchange'] == 'DCE') | (df['exchange'] == 'INE')
# df['InstrumentID'][df['lower']] = df['InstrumentID'].str.lower()
# return df
. Output only the next line. | df = wind_code_2_InstrumentID(df_csv, 'trade_hiscode') |
Given snippet: <|code_start|>"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
# 因为只用下载一次，所以都用False先关闭
# 下载行业分类列表，只用下载一次即可
if False:
download_sectors_list(w,
sector_name="中信证券一级行业指数",
root_path=__CONFIG_H5_STK_SECTOR_DIR__)
# 下载交易日，在每年的最后几周下即即可，需手工修改
if True:
resume_download_tdays(w,
enddate='2018-12-28',
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import sys
from WindPy import w
from kquant_data.config import __CONFIG_TDAYS_SSE_FILE__, __CONFIG_H5_STK_SECTOR_DIR__
from kquant_data.wind_resume.wset import download_sectors_list
from kquant_data.wind_resume.tdays import resume_download_tdays
and context:
# Path: kquant_data/config.py
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
#
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind_resume/wset.py
# def download_sectors_list(
# w,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 下载行业分类列表
# :param w:
# :param sector_name:
# :param root_path:
# :return:
# """
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# df = download_sectorconstituent(w, date_str, sector_name, None, 'wind_code')
# df['ID'] = list(range(0, df.shape[0]))
# df['ID'] += 1001
#
# path = os.path.join(root_path, '%s.csv' % sector_name)
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=None)
# return df
#
# Path: kquant_data/wind_resume/tdays.py
# def resume_download_tdays(w, enddate, path):
# """
# 增量下载
# :return:
# """
# df_old = read_tdays(path)
# if df_old is None:
# startdate = '1991-01-01'
# else:
# startdate = df_old.index[-1]
# df_new = download_tdays(w, startdate, enddate, option="")
# df = pd.concat([df_old, df_new])
#
# # 可能要‘去重’，也可能None不能参与合并
# write_tdays(path, df)
which might include code, classes, or functions. Output only the next line. | path=__CONFIG_TDAYS_SSE_FILE__) |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
# 因为只用下载一次，所以都用False先关闭
# 下载行业分类列表，只用下载一次即可
if False:
download_sectors_list(w,
sector_name="中信证券一级行业指数",
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import sys
from WindPy import w
from kquant_data.config import __CONFIG_TDAYS_SSE_FILE__, __CONFIG_H5_STK_SECTOR_DIR__
from kquant_data.wind_resume.wset import download_sectors_list
from kquant_data.wind_resume.tdays import resume_download_tdays
and context:
# Path: kquant_data/config.py
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
#
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind_resume/wset.py
# def download_sectors_list(
# w,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 下载行业分类列表
# :param w:
# :param sector_name:
# :param root_path:
# :return:
# """
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# df = download_sectorconstituent(w, date_str, sector_name, None, 'wind_code')
# df['ID'] = list(range(0, df.shape[0]))
# df['ID'] += 1001
#
# path = os.path.join(root_path, '%s.csv' % sector_name)
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=None)
# return df
#
# Path: kquant_data/wind_resume/tdays.py
# def resume_download_tdays(w, enddate, path):
# """
# 增量下载
# :return:
# """
# df_old = read_tdays(path)
# if df_old is None:
# startdate = '1991-01-01'
# else:
# startdate = df_old.index[-1]
# df_new = download_tdays(w, startdate, enddate, option="")
# df = pd.concat([df_old, df_new])
#
# # 可能要‘去重’，也可能None不能参与合并
# write_tdays(path, df)
which might include code, classes, or functions. Output only the next line. | root_path=__CONFIG_H5_STK_SECTOR_DIR__) |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
# 因为只用下载一次，所以都用False先关闭
# 下载行业分类列表，只用下载一次即可
if False:
<|code_end|>
using the current file's imports:
import sys
from WindPy import w
from kquant_data.config import __CONFIG_TDAYS_SSE_FILE__, __CONFIG_H5_STK_SECTOR_DIR__
from kquant_data.wind_resume.wset import download_sectors_list
from kquant_data.wind_resume.tdays import resume_download_tdays
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
#
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind_resume/wset.py
# def download_sectors_list(
# w,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 下载行业分类列表
# :param w:
# :param sector_name:
# :param root_path:
# :return:
# """
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# df = download_sectorconstituent(w, date_str, sector_name, None, 'wind_code')
# df['ID'] = list(range(0, df.shape[0]))
# df['ID'] += 1001
#
# path = os.path.join(root_path, '%s.csv' % sector_name)
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=None)
# return df
#
# Path: kquant_data/wind_resume/tdays.py
# def resume_download_tdays(w, enddate, path):
# """
# 增量下载
# :return:
# """
# df_old = read_tdays(path)
# if df_old is None:
# startdate = '1991-01-01'
# else:
# startdate = df_old.index[-1]
# df_new = download_tdays(w, startdate, enddate, option="")
# df = pd.concat([df_old, df_new])
#
# # 可能要‘去重’，也可能None不能参与合并
# write_tdays(path, df)
. Output only the next line. | download_sectors_list(w, |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
# 因为只用下载一次，所以都用False先关闭
# 下载行业分类列表，只用下载一次即可
if False:
download_sectors_list(w,
sector_name="中信证券一级行业指数",
root_path=__CONFIG_H5_STK_SECTOR_DIR__)
# 下载交易日，在每年的最后几周下即即可，需手工修改
if True:
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import sys
from WindPy import w
from kquant_data.config import __CONFIG_TDAYS_SSE_FILE__, __CONFIG_H5_STK_SECTOR_DIR__
from kquant_data.wind_resume.wset import download_sectors_list
from kquant_data.wind_resume.tdays import resume_download_tdays
and context:
# Path: kquant_data/config.py
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
#
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind_resume/wset.py
# def download_sectors_list(
# w,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 下载行业分类列表
# :param w:
# :param sector_name:
# :param root_path:
# :return:
# """
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# df = download_sectorconstituent(w, date_str, sector_name, None, 'wind_code')
# df['ID'] = list(range(0, df.shape[0]))
# df['ID'] += 1001
#
# path = os.path.join(root_path, '%s.csv' % sector_name)
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=None)
# return df
#
# Path: kquant_data/wind_resume/tdays.py
# def resume_download_tdays(w, enddate, path):
# """
# 增量下载
# :return:
# """
# df_old = read_tdays(path)
# if df_old is None:
# startdate = '1991-01-01'
# else:
# startdate = df_old.index[-1]
# df_new = download_tdays(w, startdate, enddate, option="")
# df = pd.concat([df_old, df_new])
#
# # 可能要‘去重’，也可能None不能参与合并
# write_tdays(path, df)
which might include code, classes, or functions. Output only the next line. | resume_download_tdays(w, |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
上期所下有原油交易中心的合约sc
sc在上市之前仿真了很久，导致下载的文件中有大量的仿真合约，需要清理
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
<|code_end|>
. Write the next line using the current file imports:
import os
import sys
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import write_constituent, read_constituent
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def write_constituent(path, df):
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=False)
#
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
, which may include functions, classes, or code. Output only the next line. | path = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, "上期所全部品种") |
Based on the snippet: <|code_start|># 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
path = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, "上期所全部品种")
for dirpath, dirnames, filenames in os.walk(path):
for filename in filenames:
if filename <= '2017-12-26.csv':
continue
# sc2018年3月26号上市
if filename >= '2018-03-26.csv':
continue
new_path = os.path.join(dirpath, filename)
df_csv = read_constituent(new_path)
sym_ex = df_csv['wind_code'].str.split('.')
sym_ex = list(sym_ex)
sym_ex_df = pd.DataFrame(sym_ex)
sym_ex_df.columns = ['InstrumentID', 'exchange']
df = pd.concat([df_csv, sym_ex_df], axis=1)
df = df[df['exchange'] != 'INE']
df = df[['wind_code']]
if len(df) < len(df_csv):
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import sys
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import write_constituent, read_constituent
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def write_constituent(path, df):
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=False)
#
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | write_constituent(new_path, df) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
上期所下有原油交易中心的合约sc
sc在上市之前仿真了很久，导致下载的文件中有大量的仿真合约，需要清理
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
path = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, "上期所全部品种")
for dirpath, dirnames, filenames in os.walk(path):
for filename in filenames:
if filename <= '2017-12-26.csv':
continue
# sc2018年3月26号上市
if filename >= '2018-03-26.csv':
continue
new_path = os.path.join(dirpath, filename)
<|code_end|>
, predict the next line using imports from the current file:
import os
import sys
import pandas as pd
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__
from kquant_data.wind.wset import write_constituent, read_constituent
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# Path: kquant_data/wind/wset.py
# def write_constituent(path, df):
# df.to_csv(path, encoding='utf-8-sig', date_format='%Y-%m-%d', index=False)
#
# def read_constituent(path):
# """
# 读取板块文件
# :param path:
# :return:
# """
# try:
# df = pd.read_csv(path, encoding='utf-8-sig', parse_dates=True)
# except Exception as e:
# return None
# try:
# df['date'] = pd.to_datetime(df['date'])
# except KeyError:
# pass
# return df
. Output only the next line. | df_csv = read_constituent(new_path) |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
调用wset函数的部分
单位累计分红可以间接拿到分红日期
w.wsd("510050.SH", "div_accumulatedperunit", "2016-01-25", "2017-02-23", "")
"""
def download_daily_at(
w,
codes,
fields,
date,
option="Fill=Previous;PriceAdj=F"):
"""
下载具体某一天的数据，这种数据一般不是很多
:param codes:
:param fields:
:param beginTime:
:param endTime:
:param columns:
:return:
"""
<|code_end|>
. Write the next line using the current file imports:
from ..processing.utils import *
from .utils import asDateTime
and context from other files:
# Path: kquant_data/wind/utils.py
# def asDateTime(v, asDate=False):
# """
# 万得中读出来的时间总多5ms，覆写这部分
# w.asDateTime = asDateTime
# w.start()
# :param v:
# :param asDate:
# :return:
# """
# # return datetime(1899, 12, 30, 0, 0, 0, 0) + timedelta(v + 0.005 / 3600 / 24)
# return datetime(1899, 12, 30, 0, 0, 0, 0) + timedelta(v)
, which may include functions, classes, or code. Output only the next line. | w.asDateTime = asDateTime |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
, which may contain function names, class names, or code. Output only the next line. | data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol']) |
Given the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
<|code_end|>
, generate the next line using the imports in this file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | _tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300)) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
bars_to_h5(data_output, df)
def export_symbols():
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sh', 'fzline')
<|code_end|>
. Use current file imports:
(import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | df_sh = get_symbols_from_path_only_stock(path, "SSE") |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
<|code_end|>
, determine the next line of code. You have imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | _tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300)) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
<|code_end|>
, predict the next line using imports from the current file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | bars_to_h5(data_output, df) |
Based on the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
lc5数据转换成h5数据放在指定目录
目前指定导出到5min_lc5
由于还得通过与5min的合并，所以这里只导出数据，不做复权处理
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 300))
try:
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | df = read_file(_tdx_path) |
Here is a snippet: <|code_start|> try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
bars_to_h5(data_output, df)
def export_symbols():
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sh', 'fzline')
df_sh = get_symbols_from_path_only_stock(path, "SSE")
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sz', 'fzline')
df_sz = get_symbols_from_path_only_stock(path, "SZSE")
df = pd.concat([df_sh, df_sz])
return df
if __name__ == '__main__':
_input = 'fzline'
_ouput = '5min_lc5'
# 由于直接5转lc5格式已经提前做了，所以这里不再需要都保存成h5再合并了
_ouput = '5min'
instruments = export_symbols()
<|code_end|>
. Write the next line using the current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
, which may include functions, classes, or code. Output only the next line. | multiprocessing_convert(True, '5min', _input, _ouput, instruments, _export_data) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
由于直接从通达信客户端中无法下载更早时间的5分钟数据，所以可以从通达信官网上先通过下载软件下载5分钟数据
然后导出，最后再合并即可
5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
, which may contain function names, class names, or code. Output only the next line. | data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol']) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
由于直接从通达信客户端中无法下载更早时间的5分钟数据，所以可以从通达信官网上先通过下载软件下载5分钟数据
然后导出，最后再合并即可
5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
, which may contain function names, class names, or code. Output only the next line. | _tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5)) |
Predict the next line after this snippet: <|code_start|>5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
bars_to_h5(data_output, df)
def export_symbols():
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sh', '5')
<|code_end|>
using the current file's imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | df_sh = get_symbols_from_path_only_stock(path, "SSE") |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
由于直接从通达信客户端中无法下载更早时间的5分钟数据，所以可以从通达信官网上先通过下载软件下载5分钟数据
然后导出，最后再合并即可
5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
<|code_end|>
. Write the next line using the current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
, which may include functions, classes, or code. Output only the next line. | _tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5)) |
Given the following code snippet before the placeholder: <|code_start|># -*- coding: utf-8 -*-
"""
由于直接从通达信客户端中无法下载更早时间的5分钟数据，所以可以从通达信官网上先通过下载软件下载5分钟数据
然后导出，最后再合并即可
5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
<|code_end|>
, predict the next line using imports from the current file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | bars_to_h5(data_output, df) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
由于直接从通达信客户端中无法下载更早时间的5分钟数据，所以可以从通达信官网上先通过下载软件下载5分钟数据
然后导出，最后再合并即可
5分钟数据下载地址
http://www.tdx.com.cn/list_66_69.html
建立与fzline同级目录的5文件夹，将数据复制进去
运行当前程序，并转换到5min_5
现在5min_lc5是最新的数据，只要将5min_5中的数据复制到5min中，然后执行合并的脚本即可
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5))
try:
<|code_end|>
, predict the next line using imports from the current file:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | df = read_file(_tdx_path) |
Based on the snippet: <|code_start|>
_tdx_path = os.path.join(__CONFIG_TDX_STK_DIR__, get_tdx_path(t['market'], t['code'], 5))
try:
df = read_file(_tdx_path)
except FileNotFoundError:
# 没有原始的数据文件
return None
# 导出到临时目录时因子都用1
df['backward_factor'] = 1
df['forward_factor'] = 1
data_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
bars_to_h5(data_output, df)
def export_symbols():
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sh', '5')
df_sh = get_symbols_from_path_only_stock(path, "SSE")
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sz', '5')
df_sz = get_symbols_from_path_only_stock(path, "SZSE")
df = pd.concat([df_sh, df_sz])
return df
if __name__ == '__main__':
_input = '5'
_ouput = '5min_5'
instruments = export_symbols()
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_TDX_STK_DIR__
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
from kquant_data.stock.tdx import get_tdx_path, bars_to_h5
from kquant_data.stock.stock import read_file
from kquant_data.processing.utils import multiprocessing_convert
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
#
# Path: kquant_data/stock/tdx.py
# def get_tdx_path(market, code, bar_size):
# # D:\new_hbzq\vipdoc\sh\lday\sh000001.day
# # D:\new_hbzq\vipdoc\sh\fzline\sh000001.lc5
# # D:\new_hbzq\vipdoc\sh\minline\sh000001.lc1
# folder = bar_size_2_folder(bar_size)
# file_ext = bar_size_2_file_ext(bar_size)
# filename = "%s%s.%s" % (market, code, file_ext)
# return os.path.join("vipdoc", market, folder, filename)
#
# def bars_to_h5(input_path, data): # 保存日线
# write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData')
# return
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
. Output only the next line. | multiprocessing_convert(True, '5min', _input, _ouput, instruments, _export_data) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载合约乘数和执行价
"""
def get_sector_at(df_info, date):
idx = (df_info['listed_date'] <= date) & (df_info['expire_date'] >= date)
df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
<|code_end|>
, predict the next line using imports from the current file:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date) |
Given the following code snippet before the placeholder: <|code_start|>
def get_sector_at(df_info, date):
idx = (df_info['listed_date'] <= date) & (df_info['expire_date'] >= date)
df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date)
if os.path.exists(path):
return
print('准备下载数据')
df = download_optionchain(w, date, '510050.SH')
write_data_dataframe(path, df)
if __name__ == '__main__':
w.start()
# 获取期权基础信息文件
df_info = get_opt_info('510050.SH.csv')
# 得到除权日
<|code_end|>
, predict the next line using imports from the current file:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | path = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend', 'sh510050.h5') |
Using the snippet: <|code_start|>下载合约乘数和执行价
"""
def get_sector_at(df_info, date):
idx = (df_info['listed_date'] <= date) & (df_info['expire_date'] >= date)
df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date)
if os.path.exists(path):
return
print('准备下载数据')
df = download_optionchain(w, date, '510050.SH')
write_data_dataframe(path, df)
if __name__ == '__main__':
w.start()
# 获取期权基础信息文件
<|code_end|>
, determine the next line of code. You have imports:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | df_info = get_opt_info('510050.SH.csv') |
Given the code snippet: <|code_start|> df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date)
if os.path.exists(path):
return
print('准备下载数据')
df = download_optionchain(w, date, '510050.SH')
write_data_dataframe(path, df)
if __name__ == '__main__':
w.start()
# 获取期权基础信息文件
df_info = get_opt_info('510050.SH.csv')
# 得到除权日
path = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend', 'sh510050.h5')
div = pd.read_hdf(path)
for i in range(div.shape[0]):
<|code_end|>
, generate the next line using the imports in this file:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | date_right = yyyyMMddHHmm_2_datetime(div.ix[i, 'time']) |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载合约乘数和执行价
"""
def get_sector_at(df_info, date):
idx = (df_info['listed_date'] <= date) & (df_info['expire_date'] >= date)
df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date)
if os.path.exists(path):
return
print('准备下载数据')
df = download_optionchain(w, date, '510050.SH')
<|code_end|>
, predict the next line using imports from the current file:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | write_data_dataframe(path, df) |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载合约乘数和执行价
"""
def get_sector_at(df_info, date):
idx = (df_info['listed_date'] <= date) & (df_info['expire_date'] >= date)
df_info2 = df_info[idx]
if df_info2.empty:
return None
# print(df_info2)
return df_info2
def download_exe_price(w, sector, date):
if sector is None:
return
path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optionchain', '510050.SH', '%s.csv' % date)
if os.path.exists(path):
return
print('准备下载数据')
<|code_end|>
, determine the next line of code. You have imports:
from WindPy import w
from kquant_data.config import __CONFIG_H5_OPT_DIR__, __CONFIG_H5_STK_DIR__
from kquant_data.option.info import get_opt_info
from kquant_data.utils.xdatetime import yyyyMMddHHmm_2_datetime
from kquant_data.xio.csv import write_data_dataframe
from kquant_data.wind.wset import download_optionchain
import os
import pandas as pd
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_OPT_DIR__ = r'D:\DATA_OPT'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/option/info.py
# def get_opt_info(filename):
# root_path = os.path.join(__CONFIG_H5_OPT_DIR__, 'optioncontractbasicinfo', filename)
# df_info = read_optioncontractbasicinfo(root_path)
# # 排序一下，方便显示，先按月份，然再换名后的月份
# df_info = df_info.sort_values(by=['limit_month', 'limit_month_m', 'call_or_put', 'exercise_price'])
# return df_info
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
#
# Path: kquant_data/xio/csv.py
# def write_data_dataframe(path, df, date_format='%Y-%m-%d'):
# """
# 写入数据
# :param path:
# :param df:
# :return:
# """
# df.to_csv(path, date_format=date_format, encoding='utf-8-sig')
#
# Path: kquant_data/wind/wset.py
# def download_optionchain(w, date='2017-11-28', us_code='510050.SH',
# field='option_code,option_name,strike_price,multiplier'):
# """
# 下载指定日期期权数据
#
# w_wset_data = vba_wset("optionchain","date=2017-11-28;us_code=510050.SH;option_var=全部;call_put=全部;field=option_code,option_name,strike_price,multiplier",)
# :param w:
# :param windcode:
# :param date:
# :return:
# """
# param = 'date=%s' % date
# param += ';us_code=%s' % us_code
# if field:
# param += ';field=%s' % field
#
# w.asDateTime = asDateTime
# w_wset_data = w.wset("optionchain", param)
# df = pd.DataFrame(w_wset_data.Data)
# df = df.T
# df.columns = w_wset_data.Fields
# return df
. Output only the next line. | df = download_optionchain(w, date, '510050.SH') |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载财报一类的信息
"""
if __name__ == '__main__':
w.start()
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
<|code_end|>
. Use current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.api import all_instruments
from kquant_data.wind_resume.wsd import resume_download_financial_report_quarter
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
and context (classes, functions, or code) from other files:
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_financial_report_quarter(w, wind_codes, root_path, fields=['stm_issuingdate'], dtype=np.datetime64):
# """
#
# 其实发现有更新的部分，然后再增量下载其它字段是最合适不过的了，目前此功能还没有实现
# :param w:
# :param wind_codes:
# :return:
# """
# dr = pd.date_range(end=datetime.today().date(), periods=4, freq='Q')
# new_end_str = dr[-1].strftime('%Y-%m-%d')
#
# for field in fields:
# resume_download_daily_many_to_one_file(w,
# wind_codes,
# field,
# dtype, new_end_str,
# root_path,
# option='unit=1;rptType=1;Period=Q;Days=Alldays')
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
. Output only the next line. | Symbols = all_instruments(path) |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载财报一类的信息
"""
if __name__ == '__main__':
w.start()
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
Symbols = all_instruments(path)
wind_codes = list(Symbols['wind_code'])
# 下载时间类型的数据
fields = ['stm_issuingdate']
if True:
<|code_end|>
. Use current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.api import all_instruments
from kquant_data.wind_resume.wsd import resume_download_financial_report_quarter
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
and context (classes, functions, or code) from other files:
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_financial_report_quarter(w, wind_codes, root_path, fields=['stm_issuingdate'], dtype=np.datetime64):
# """
#
# 其实发现有更新的部分，然后再增量下载其它字段是最合适不过的了，目前此功能还没有实现
# :param w:
# :param wind_codes:
# :return:
# """
# dr = pd.date_range(end=datetime.today().date(), periods=4, freq='Q')
# new_end_str = dr[-1].strftime('%Y-%m-%d')
#
# for field in fields:
# resume_download_daily_many_to_one_file(w,
# wind_codes,
# field,
# dtype, new_end_str,
# root_path,
# option='unit=1;rptType=1;Period=Q;Days=Alldays')
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
. Output only the next line. | resume_download_financial_report_quarter(w, wind_codes, __CONFIG_H5_STK_FACTOR_DIR__, fields=fields, |
Given the following code snippet before the placeholder: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载财报一类的信息
"""
if __name__ == '__main__':
w.start()
<|code_end|>
, predict the next line using imports from the current file:
import os
import numpy as np
from WindPy import w
from kquant_data.api import all_instruments
from kquant_data.wind_resume.wsd import resume_download_financial_report_quarter
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
and context including class names, function names, and sometimes code from other files:
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_financial_report_quarter(w, wind_codes, root_path, fields=['stm_issuingdate'], dtype=np.datetime64):
# """
#
# 其实发现有更新的部分，然后再增量下载其它字段是最合适不过的了，目前此功能还没有实现
# :param w:
# :param wind_codes:
# :return:
# """
# dr = pd.date_range(end=datetime.today().date(), periods=4, freq='Q')
# new_end_str = dr[-1].strftime('%Y-%m-%d')
#
# for field in fields:
# resume_download_daily_many_to_one_file(w,
# wind_codes,
# field,
# dtype, new_end_str,
# root_path,
# option='unit=1;rptType=1;Period=Q;Days=Alldays')
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
. Output only the next line. | path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv') |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载财报一类的信息
"""
if __name__ == '__main__':
w.start()
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
Symbols = all_instruments(path)
wind_codes = list(Symbols['wind_code'])
# 下载时间类型的数据
fields = ['stm_issuingdate']
if True:
<|code_end|>
. Write the next line using the current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.api import all_instruments
from kquant_data.wind_resume.wsd import resume_download_financial_report_quarter
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
and context from other files:
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_financial_report_quarter(w, wind_codes, root_path, fields=['stm_issuingdate'], dtype=np.datetime64):
# """
#
# 其实发现有更新的部分，然后再增量下载其它字段是最合适不过的了，目前此功能还没有实现
# :param w:
# :param wind_codes:
# :return:
# """
# dr = pd.date_range(end=datetime.today().date(), periods=4, freq='Q')
# new_end_str = dr[-1].strftime('%Y-%m-%d')
#
# for field in fields:
# resume_download_daily_many_to_one_file(w,
# wind_codes,
# field,
# dtype, new_end_str,
# root_path,
# option='unit=1;rptType=1;Period=Q;Days=Alldays')
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
, which may include functions, classes, or code. Output only the next line. | resume_download_financial_report_quarter(w, wind_codes, __CONFIG_H5_STK_FACTOR_DIR__, fields=fields, |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
日数据和权息数全都导出
用户直接读取h5格式的数据即可
"""
def export_daily():
"""
只导出有除权信息的股票，没有导出的数据，以后直接读通达信
:return:
"""
# 复权因子的导出
dividend_input = os.path.join(__CONFIG_H5_STK_DIR__, 'gbbq.csv')
<|code_end|>
. Write the next line using the current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_DZH_PWR_FILE__, __CONFIG_H5_STK_DIVIDEND_DIR__, __CONFIG_TDX_STK_DIR__, \
__CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import export_dividend_daily_gbbq
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_DZH_PWR_FILE__ = r"D:\dzh2\Download\PWR\full.PWR"
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
# """
# 导出除权数据，并同时生成对应的日线数据
# :param tdx_input:
# :param dzh_input:
# :param dzh_output:
# :param daily_output:
# :return:
# """
# df = pd.read_csv(gbbq_input, index_col=0, dtype={'code': str})
# # 只取除权信息
# df = df[df['category'] == 1]
# df['exchange'] = df['market'].replace(0, "sz").replace(1, 'sh')
# df['symbol'] = df['exchange'] + df['code']
# div_list = [(name, group) for name, group in df.groupby(by=['symbol'])]
#
# tic()
#
# multi = True
# if multi:
# # 多进程并行计算
# pool_size = multiprocessing.cpu_count()
# if pool_size > 2:
# pool_size -= 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(_export_dividend_from_data, tdx_root, dividend_output, daily_output)
# pool_outputs = pool.map(func, div_list)
# print('Pool:', pool_outputs)
# else:
# # 单线程
# for d in div_list:
# _export_dividend_from_data(tdx_root, dividend_output, daily_output, d)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
, which may include functions, classes, or code. Output only the next line. | dividend_output = __CONFIG_H5_STK_DIVIDEND_DIR__ |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
日数据和权息数全都导出
用户直接读取h5格式的数据即可
"""
def export_daily():
"""
只导出有除权信息的股票，没有导出的数据，以后直接读通达信
:return:
"""
# 复权因子的导出
dividend_input = os.path.join(__CONFIG_H5_STK_DIR__, 'gbbq.csv')
dividend_output = __CONFIG_H5_STK_DIVIDEND_DIR__
<|code_end|>
, determine the next line of code. You have imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_DZH_PWR_FILE__, __CONFIG_H5_STK_DIVIDEND_DIR__, __CONFIG_TDX_STK_DIR__, \
__CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import export_dividend_daily_gbbq
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_DZH_PWR_FILE__ = r"D:\dzh2\Download\PWR\full.PWR"
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
# """
# 导出除权数据，并同时生成对应的日线数据
# :param tdx_input:
# :param dzh_input:
# :param dzh_output:
# :param daily_output:
# :return:
# """
# df = pd.read_csv(gbbq_input, index_col=0, dtype={'code': str})
# # 只取除权信息
# df = df[df['category'] == 1]
# df['exchange'] = df['market'].replace(0, "sz").replace(1, 'sh')
# df['symbol'] = df['exchange'] + df['code']
# div_list = [(name, group) for name, group in df.groupby(by=['symbol'])]
#
# tic()
#
# multi = True
# if multi:
# # 多进程并行计算
# pool_size = multiprocessing.cpu_count()
# if pool_size > 2:
# pool_size -= 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(_export_dividend_from_data, tdx_root, dividend_output, daily_output)
# pool_outputs = pool.map(func, div_list)
# print('Pool:', pool_outputs)
# else:
# # 单线程
# for d in div_list:
# _export_dividend_from_data(tdx_root, dividend_output, daily_output, d)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
. Output only the next line. | daily_input = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc") |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
日数据和权息数全都导出
用户直接读取h5格式的数据即可
"""
def export_daily():
"""
只导出有除权信息的股票，没有导出的数据，以后直接读通达信
:return:
"""
# 复权因子的导出
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_DZH_PWR_FILE__, __CONFIG_H5_STK_DIVIDEND_DIR__, __CONFIG_TDX_STK_DIR__, \
__CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import export_dividend_daily_gbbq
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
and context:
# Path: kquant_data/config.py
# __CONFIG_DZH_PWR_FILE__ = r"D:\dzh2\Download\PWR\full.PWR"
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
# """
# 导出除权数据，并同时生成对应的日线数据
# :param tdx_input:
# :param dzh_input:
# :param dzh_output:
# :param daily_output:
# :return:
# """
# df = pd.read_csv(gbbq_input, index_col=0, dtype={'code': str})
# # 只取除权信息
# df = df[df['category'] == 1]
# df['exchange'] = df['market'].replace(0, "sz").replace(1, 'sh')
# df['symbol'] = df['exchange'] + df['code']
# div_list = [(name, group) for name, group in df.groupby(by=['symbol'])]
#
# tic()
#
# multi = True
# if multi:
# # 多进程并行计算
# pool_size = multiprocessing.cpu_count()
# if pool_size > 2:
# pool_size -= 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(_export_dividend_from_data, tdx_root, dividend_output, daily_output)
# pool_outputs = pool.map(func, div_list)
# print('Pool:', pool_outputs)
# else:
# # 单线程
# for d in div_list:
# _export_dividend_from_data(tdx_root, dividend_output, daily_output, d)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
which might include code, classes, or functions. Output only the next line. | dividend_input = os.path.join(__CONFIG_H5_STK_DIR__, 'gbbq.csv') |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
日数据和权息数全都导出
用户直接读取h5格式的数据即可
"""
def export_daily():
"""
只导出有除权信息的股票，没有导出的数据，以后直接读通达信
:return:
"""
# 复权因子的导出
dividend_input = os.path.join(__CONFIG_H5_STK_DIR__, 'gbbq.csv')
dividend_output = __CONFIG_H5_STK_DIVIDEND_DIR__
daily_input = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc")
daily_output = os.path.join(__CONFIG_H5_STK_DIR__, "1day")
# export_dividend_daily(dividend_input, daily_input, dividend_output, daily_output)
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_DZH_PWR_FILE__, __CONFIG_H5_STK_DIVIDEND_DIR__, __CONFIG_TDX_STK_DIR__, \
__CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import export_dividend_daily_gbbq
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
and context:
# Path: kquant_data/config.py
# __CONFIG_DZH_PWR_FILE__ = r"D:\dzh2\Download\PWR\full.PWR"
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
# """
# 导出除权数据，并同时生成对应的日线数据
# :param tdx_input:
# :param dzh_input:
# :param dzh_output:
# :param daily_output:
# :return:
# """
# df = pd.read_csv(gbbq_input, index_col=0, dtype={'code': str})
# # 只取除权信息
# df = df[df['category'] == 1]
# df['exchange'] = df['market'].replace(0, "sz").replace(1, 'sh')
# df['symbol'] = df['exchange'] + df['code']
# div_list = [(name, group) for name, group in df.groupby(by=['symbol'])]
#
# tic()
#
# multi = True
# if multi:
# # 多进程并行计算
# pool_size = multiprocessing.cpu_count()
# if pool_size > 2:
# pool_size -= 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(_export_dividend_from_data, tdx_root, dividend_output, daily_output)
# pool_outputs = pool.map(func, div_list)
# print('Pool:', pool_outputs)
# else:
# # 单线程
# for d in div_list:
# _export_dividend_from_data(tdx_root, dividend_output, daily_output, d)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
which might include code, classes, or functions. Output only the next line. | export_dividend_daily_gbbq(dividend_input, daily_input, dividend_output, daily_output) |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
日数据和权息数全都导出
用户直接读取h5格式的数据即可
"""
def export_daily():
"""
只导出有除权信息的股票，没有导出的数据，以后直接读通达信
:return:
"""
# 复权因子的导出
dividend_input = os.path.join(__CONFIG_H5_STK_DIR__, 'gbbq.csv')
dividend_output = __CONFIG_H5_STK_DIVIDEND_DIR__
daily_input = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc")
daily_output = os.path.join(__CONFIG_H5_STK_DIR__, "1day")
# export_dividend_daily(dividend_input, daily_input, dividend_output, daily_output)
export_dividend_daily_gbbq(dividend_input, daily_input, dividend_output, daily_output)
def export_symbols():
path = os.path.join(__CONFIG_TDX_STK_DIR__, "vipdoc", 'sh', 'lday')
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_DZH_PWR_FILE__, __CONFIG_H5_STK_DIVIDEND_DIR__, __CONFIG_TDX_STK_DIR__, \
__CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import export_dividend_daily_gbbq
from kquant_data.stock.symbol import get_symbols_from_path_only_stock
and context:
# Path: kquant_data/config.py
# __CONFIG_DZH_PWR_FILE__ = r"D:\dzh2\Download\PWR\full.PWR"
#
# __CONFIG_H5_STK_DIVIDEND_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'dividend')
#
# __CONFIG_TDX_STK_DIR__ = r'D:\new_hbzq'
#
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
# """
# 导出除权数据，并同时生成对应的日线数据
# :param tdx_input:
# :param dzh_input:
# :param dzh_output:
# :param daily_output:
# :return:
# """
# df = pd.read_csv(gbbq_input, index_col=0, dtype={'code': str})
# # 只取除权信息
# df = df[df['category'] == 1]
# df['exchange'] = df['market'].replace(0, "sz").replace(1, 'sh')
# df['symbol'] = df['exchange'] + df['code']
# div_list = [(name, group) for name, group in df.groupby(by=['symbol'])]
#
# tic()
#
# multi = True
# if multi:
# # 多进程并行计算
# pool_size = multiprocessing.cpu_count()
# if pool_size > 2:
# pool_size -= 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(_export_dividend_from_data, tdx_root, dividend_output, daily_output)
# pool_outputs = pool.map(func, div_list)
# print('Pool:', pool_outputs)
# else:
# # 单线程
# for d in div_list:
# _export_dividend_from_data(tdx_root, dividend_output, daily_output, d)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path_only_stock(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# if is_stock(filename):
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
which might include code, classes, or functions. Output only the next line. | df_sh = get_symbols_from_path_only_stock(path, "SSE") |
Based on the snippet: <|code_start|># -*- coding: utf-8 -*-
"""
处理通达信数据相关的操作
http://www.cnblogs.com/zeroone/archive/2013/07/10/3181251.html
文件路径
-------
D:\new_hbzq\vipdoc\sh\lday
D:\new_hbzq\vipdoc\sh\fzline
D:\new_hbzq\vipdoc\sh\minline
"""
# 保存成h5格式时的类型
tdx_h5_type = np.dtype([
('DateTime', np.uint64),
('Open', np.float32),
('High', np.float32),
('Low', np.float32),
('Close', np.float32),
('Amount', np.float32),
('Volume', np.uint32),
('backward_factor', np.float32),
('forward_factor', np.float32),
])
def bars_to_h5(input_path, data): # 保存日线
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import numpy as np
import pandas as pd
from ..xio.h5 import write_dataframe_set_struct_keep_head
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_struct_keep_head(path, data, dtype, dateset_name):
# """
# 保存DataFrame数据
# 保留表头
# 可以用来存K线，除权除息等信息
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
#
# r = data.to_records(index=False)
# d = np.array(r, dtype=dtype)
#
# f.create_dataset(dateset_name, data=d, compression="gzip", compression_opts=6)
# f.close()
# return
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
. Output only the next line. | write_dataframe_set_struct_keep_head(input_path, data, tdx_h5_type, 'BarData') |
Based on the snippet: <|code_start|>
def read_file(path, instrument_type='stock'):
"""
http://www.tdx.com.cn/list_66_68.html
通达信本地目录有day/lc1/lc5三种后缀名，两种格式
从通达信官网下载的5分钟后缀只有5这种格式，为了处理方便，时间精度都只到分钟
:param path:
:return:
"""
columns = ['DateTime', 'Open', 'High', 'Low', 'Close', 'Amount', 'Volume', 'na']
file_ext = os.path.splitext(path)[1][1:]
if instrument_type == 'stock':
ohlc_type = {'day': 'i4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
formats = ['i4'] + [ohlc_type] * 4 + ['f4'] + ['i4'] * 2
elif instrument_type == 'option':
ohlc_type = {'day': 'f4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
formats = ['i4'] + [ohlc_type] * 4 + ['i4'] + ['i4'] * 2
date_parser = {'day': day_datetime_long,
'5': min_datetime_long,
'lc1': min_datetime_long,
'lc5': min_datetime_long,
}[file_ext]
dtype = np.dtype({'names': columns, 'formats': formats})
data = np.fromfile(path, dtype=dtype)
df = pd.DataFrame(data)
# 为了处理的方便，存一套long类型的时间
df['DateTime'] = df['DateTime'].apply(date_parser)
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import numpy as np
import pandas as pd
from ..xio.h5 import write_dataframe_set_struct_keep_head
from ..utils.xdatetime import yyyyMMddHHmm_2_datetime
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_struct_keep_head(path, data, dtype, dateset_name):
# """
# 保存DataFrame数据
# 保留表头
# 可以用来存K线，除权除息等信息
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
#
# r = data.to_records(index=False)
# d = np.array(r, dtype=dtype)
#
# f.create_dataset(dateset_name, data=d, compression="gzip", compression_opts=6)
# f.close()
# return
#
# Path: kquant_data/utils/xdatetime.py
# def yyyyMMddHHmm_2_datetime(dt):
# """
# 输入一个长整型yyyyMMddhmm，返回对应的时间
# :param dt:
# :return:
# """
# dt = int(dt) # FIXME：在python2下会有问题吗？
# (yyyyMMdd, hh) = divmod(dt, 10000)
# (yyyy, MMdd) = divmod(yyyyMMdd, 10000)
# (MM, dd) = divmod(MMdd, 100)
# (hh, mm) = divmod(hh, 100)
#
# return datetime(yyyy, MM, dd, hh, mm)
. Output only the next line. | df['datetime'] = df['DateTime'].apply(yyyyMMddHHmm_2_datetime) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
演示将5分钟数据转成1小时数据
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
<|code_end|>
. Use current file imports:
(import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import bars_to_h5
from kquant_data.processing.utils import filter_dataframe, bar_convert, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def bar_convert(df, rule='1H'):
# """
# 数据转换
# :param df:
# :param rule:
# :return:
# """
# how_dict = {
# 'DateTime': 'first',
# 'Open': 'first',
# 'High': 'max',
# 'Low': 'min',
# 'Close': 'last',
# 'Amount': 'sum',
# 'Volume': 'sum',
# 'backward_factor': 'last',
# 'forward_factor': 'last',
# }
# # how_dict = {
# # 'Symbol': 'first',
# # 'DateTime': 'first',
# # 'TradingDay': 'first',
# # 'ActionDay': 'first',
# # 'Time': 'first',
# # 'BarSize': 'first',
# # 'Pad': 'min',
# # 'Open': 'first',
# # 'High': 'max',
# # 'Low': 'min',
# # 'Close': 'last',
# # 'Volume': 'sum',
# # 'Amount': 'sum',
# # 'OpenInterest': 'last',
# # 'Settle': 'last',
# # 'AdjustFactorPM': 'last',
# # 'AdjustFactorTD': 'last',
# # 'BAdjustFactorPM': 'last',
# # 'BAdjustFactorTD': 'last',
# # 'FAdjustFactorPM': 'last',
# # 'FAdjustFactorTD': 'last',
# # 'MoneyFlow': 'sum',
# # }
# columns = df.columns
# new = df.resample(rule, closed='left', label='left').apply(how_dict)
#
# new.dropna(inplace=True)
# # 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
# new = new[new['Open'] != 0]
#
# # 居然位置要调整一下
# new = new[columns]
#
# # 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
# new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
#
# return new
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | path = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol']) |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
演示将5分钟数据转成1小时数据
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
path = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol'])
df = None
try:
df = pd.read_hdf(path)
except:
pass
if df is None:
return None
df = filter_dataframe(df, 'DateTime', None, None, None)
df1 = bar_convert(df, rule)
date_output = os.path.join(__CONFIG_H5_STK_DIR__, output, t['market'], "%s.h5" % t['local_symbol'])
<|code_end|>
using the current file's imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import bars_to_h5
from kquant_data.processing.utils import filter_dataframe, bar_convert, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def bar_convert(df, rule='1H'):
# """
# 数据转换
# :param df:
# :param rule:
# :return:
# """
# how_dict = {
# 'DateTime': 'first',
# 'Open': 'first',
# 'High': 'max',
# 'Low': 'min',
# 'Close': 'last',
# 'Amount': 'sum',
# 'Volume': 'sum',
# 'backward_factor': 'last',
# 'forward_factor': 'last',
# }
# # how_dict = {
# # 'Symbol': 'first',
# # 'DateTime': 'first',
# # 'TradingDay': 'first',
# # 'ActionDay': 'first',
# # 'Time': 'first',
# # 'BarSize': 'first',
# # 'Pad': 'min',
# # 'Open': 'first',
# # 'High': 'max',
# # 'Low': 'min',
# # 'Close': 'last',
# # 'Volume': 'sum',
# # 'Amount': 'sum',
# # 'OpenInterest': 'last',
# # 'Settle': 'last',
# # 'AdjustFactorPM': 'last',
# # 'AdjustFactorTD': 'last',
# # 'BAdjustFactorPM': 'last',
# # 'BAdjustFactorTD': 'last',
# # 'FAdjustFactorPM': 'last',
# # 'FAdjustFactorTD': 'last',
# # 'MoneyFlow': 'sum',
# # }
# columns = df.columns
# new = df.resample(rule, closed='left', label='left').apply(how_dict)
#
# new.dropna(inplace=True)
# # 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
# new = new[new['Open'] != 0]
#
# # 居然位置要调整一下
# new = new[columns]
#
# # 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
# new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
#
# return new
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
. Output only the next line. | bars_to_h5(date_output, df1) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
演示将5分钟数据转成1小时数据
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
path = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol'])
df = None
try:
df = pd.read_hdf(path)
except:
pass
if df is None:
return None
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import bars_to_h5
from kquant_data.processing.utils import filter_dataframe, bar_convert, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def bar_convert(df, rule='1H'):
# """
# 数据转换
# :param df:
# :param rule:
# :return:
# """
# how_dict = {
# 'DateTime': 'first',
# 'Open': 'first',
# 'High': 'max',
# 'Low': 'min',
# 'Close': 'last',
# 'Amount': 'sum',
# 'Volume': 'sum',
# 'backward_factor': 'last',
# 'forward_factor': 'last',
# }
# # how_dict = {
# # 'Symbol': 'first',
# # 'DateTime': 'first',
# # 'TradingDay': 'first',
# # 'ActionDay': 'first',
# # 'Time': 'first',
# # 'BarSize': 'first',
# # 'Pad': 'min',
# # 'Open': 'first',
# # 'High': 'max',
# # 'Low': 'min',
# # 'Close': 'last',
# # 'Volume': 'sum',
# # 'Amount': 'sum',
# # 'OpenInterest': 'last',
# # 'Settle': 'last',
# # 'AdjustFactorPM': 'last',
# # 'AdjustFactorTD': 'last',
# # 'BAdjustFactorPM': 'last',
# # 'BAdjustFactorTD': 'last',
# # 'FAdjustFactorPM': 'last',
# # 'FAdjustFactorTD': 'last',
# # 'MoneyFlow': 'sum',
# # }
# columns = df.columns
# new = df.resample(rule, closed='left', label='left').apply(how_dict)
#
# new.dropna(inplace=True)
# # 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
# new = new[new['Open'] != 0]
#
# # 居然位置要调整一下
# new = new[columns]
#
# # 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
# new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
#
# return new
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
, which may contain function names, class names, or code. Output only the next line. | df = filter_dataframe(df, 'DateTime', None, None, None) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
演示将5分钟数据转成1小时数据
"""
def _export_data(rule, _input, output, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
path = os.path.join(__CONFIG_H5_STK_DIR__, _input, t['market'], "%s.h5" % t['local_symbol'])
df = None
try:
df = pd.read_hdf(path)
except:
pass
if df is None:
return None
df = filter_dataframe(df, 'DateTime', None, None, None)
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.stock.stock import bars_to_h5
from kquant_data.processing.utils import filter_dataframe, bar_convert, multiprocessing_convert
from kquant_data.stock.symbol import get_folder_symbols
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/stock/stock.py
# def sort_dividend(divs):
# def factor(daily, divs, ndigits):
# def adjust(df, adjust_type=None):
# def merge_adjust_factor(df, div):
# def read_h5_tdx(market, code, bar_size, h5_path, tdx_path, div_path):
# def _export_dividend_from_data(tdx_root, dividend_output, daily_output, data):
# def export_dividend_daily_dzh(dzh_input, tdx_root, dividend_output, daily_output):
# def export_dividend_daily_gbbq(gbbq_input, tdx_root, dividend_output, daily_output):
#
# Path: kquant_data/processing/utils.py
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# def bar_convert(df, rule='1H'):
# """
# 数据转换
# :param df:
# :param rule:
# :return:
# """
# how_dict = {
# 'DateTime': 'first',
# 'Open': 'first',
# 'High': 'max',
# 'Low': 'min',
# 'Close': 'last',
# 'Amount': 'sum',
# 'Volume': 'sum',
# 'backward_factor': 'last',
# 'forward_factor': 'last',
# }
# # how_dict = {
# # 'Symbol': 'first',
# # 'DateTime': 'first',
# # 'TradingDay': 'first',
# # 'ActionDay': 'first',
# # 'Time': 'first',
# # 'BarSize': 'first',
# # 'Pad': 'min',
# # 'Open': 'first',
# # 'High': 'max',
# # 'Low': 'min',
# # 'Close': 'last',
# # 'Volume': 'sum',
# # 'Amount': 'sum',
# # 'OpenInterest': 'last',
# # 'Settle': 'last',
# # 'AdjustFactorPM': 'last',
# # 'AdjustFactorTD': 'last',
# # 'BAdjustFactorPM': 'last',
# # 'BAdjustFactorTD': 'last',
# # 'FAdjustFactorPM': 'last',
# # 'FAdjustFactorTD': 'last',
# # 'MoneyFlow': 'sum',
# # }
# columns = df.columns
# new = df.resample(rule, closed='left', label='left').apply(how_dict)
#
# new.dropna(inplace=True)
# # 有些才上市没多久的，居然开头有很多天为空白，需要删除,如sh603990
# new = new[new['Open'] != 0]
#
# # 居然位置要调整一下
# new = new[columns]
#
# # 由于存盘时是用的DateTime这个字段，不是index上的时间，这会导致其它软件读取数据时出错，需要修正数据
# new['DateTime'] = new.index.map(datetime_2_yyyyMMddHHmm)
#
# return new
#
# def multiprocessing_convert(multi, rule, _input, output, instruments, func_convert):
# tic()
#
# if multi:
# pool_size = multiprocessing.cpu_count() - 1
# pool = multiprocessing.Pool(processes=pool_size)
# func = partial(func_convert, rule, _input, output, instruments)
# pool_outputs = pool.map(func, range(len(instruments)))
# print('Pool:', pool_outputs)
# else:
# for i in range(len(instruments)):
# func_convert(rule, _input, output, instruments, i)
#
# toc()
#
# Path: kquant_data/stock/symbol.py
# def get_folder_symbols(folder, sub_folder):
# path = os.path.join(folder, sub_folder, 'sh')
# df_sh = get_symbols_from_path(path, "SSE")
# path = os.path.join(folder, sub_folder, 'sz')
# df_sz = get_symbols_from_path(path, "SZSE")
# df = pd.concat([df_sh, df_sz])
#
# return df
, which may contain function names, class names, or code. Output only the next line. | df1 = bar_convert(df, rule) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
全市场计算权重
"""
def merge_weight_000300():
rule = '1day'
wind_code = '000300.SH'
<|code_end|>
with the help of current file imports:
from kquant_data.processing.merge import merge_weight
and context from other files:
# Path: kquant_data/processing/merge.py
# def merge_weight(rule, wind_code, dataset_name):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'Symbol.csv')
# symbols = all_instruments(path)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'DateTime.csv')
# DateTime = get_datetime(path)
#
# df = merge_weight_internal(symbols, DateTime, wind_code)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, "%s.h5" % dataset_name)
# write_dataframe_set_dtype_remove_head(path, df, None, dataset_name)
#
# toc()
, which may contain function names, class names, or code. Output only the next line. | merge_weight(rule, wind_code, 'weight') |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
分为两种
1. 全市场数据，将部分标记上权重
2. 只对历史上成为成份股的，进行处理，由于前面已经转换了数据，这里只要跳选数据并处理即可
以前的做法是先生成数据，然后再生成合约
"""
if __name__ == '__main__':
# 时间和合约都已经生成了
# 只要将时间与合约对上即可
<|code_end|>
with the help of current file imports:
import os
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.processing.merge import merge_weight_internal
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import write_dataframe_set_dtype_remove_head
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/processing/merge.py
# def merge_weight_internal(symbols, DateTime, wind_code):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# tic()
# path = os.path.join(__CONFIG_H5_STK_WEIGHT_DIR__, wind_code)
# df = load_index_weight(path)
# print("数据加载完成")
# # 与行业不同，行业是全部有数据，它是有一部分有数据，所以直接用fillna会出错，需要先填充
# df.fillna(-1, inplace=True)
# toc()
#
# # 原始数据比较简单，但与行业板块数据又不一样
# # 1.每年的约定时间会调整成份股
# # 2.每天的值都不一样
# # 约定nan表示不属于成份，0表示属于成份，但权重为0
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # -1表示特殊数据，处理下
# df.replace(-1, np.nan, inplace=True)
# print("数据加载完成")
# toc()
#
# return df
#
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_dtype_remove_head(path, data, dtype, dataset_name):
# """
# 每个单元格的数据类型都一样
# 强行指定类型可以让文件的占用更小
# 表头不保存
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
# if dtype is None:
# f.create_dataset(dataset_name, data=data.as_matrix(), compression="gzip", compression_opts=6)
# else:
# f.create_dataset(dataset_name, data=data, compression="gzip", compression_opts=6, dtype=dtype)
# f.close()
# return
, which may contain function names, class names, or code. Output only the next line. | path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'Symbol.csv') |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
分为两种
1. 全市场数据，将部分标记上权重
2. 只对历史上成为成份股的，进行处理，由于前面已经转换了数据，这里只要跳选数据并处理即可
以前的做法是先生成数据，然后再生成合约
"""
if __name__ == '__main__':
# 时间和合约都已经生成了
# 只要将时间与合约对上即可
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'Symbol.csv')
symbols = all_instruments(path)
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'DateTime.csv')
DateTime = get_datetime(path)
<|code_end|>
using the current file's imports:
import os
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.processing.merge import merge_weight_internal
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import write_dataframe_set_dtype_remove_head
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/processing/merge.py
# def merge_weight_internal(symbols, DateTime, wind_code):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# tic()
# path = os.path.join(__CONFIG_H5_STK_WEIGHT_DIR__, wind_code)
# df = load_index_weight(path)
# print("数据加载完成")
# # 与行业不同，行业是全部有数据，它是有一部分有数据，所以直接用fillna会出错，需要先填充
# df.fillna(-1, inplace=True)
# toc()
#
# # 原始数据比较简单，但与行业板块数据又不一样
# # 1.每年的约定时间会调整成份股
# # 2.每天的值都不一样
# # 约定nan表示不属于成份，0表示属于成份，但权重为0
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # -1表示特殊数据，处理下
# df.replace(-1, np.nan, inplace=True)
# print("数据加载完成")
# toc()
#
# return df
#
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_dtype_remove_head(path, data, dtype, dataset_name):
# """
# 每个单元格的数据类型都一样
# 强行指定类型可以让文件的占用更小
# 表头不保存
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
# if dtype is None:
# f.create_dataset(dataset_name, data=data.as_matrix(), compression="gzip", compression_opts=6)
# else:
# f.create_dataset(dataset_name, data=data, compression="gzip", compression_opts=6, dtype=dtype)
# f.close()
# return
. Output only the next line. | df = merge_weight_internal(symbols, DateTime, "000300.SH") |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
分为两种
1. 全市场数据，将部分标记上权重
2. 只对历史上成为成份股的，进行处理，由于前面已经转换了数据，这里只要跳选数据并处理即可
以前的做法是先生成数据，然后再生成合约
"""
if __name__ == '__main__':
# 时间和合约都已经生成了
# 只要将时间与合约对上即可
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'Symbol.csv')
symbols = all_instruments(path)
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'DateTime.csv')
<|code_end|>
, determine the next line of code. You have imports:
import os
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.processing.merge import merge_weight_internal
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import write_dataframe_set_dtype_remove_head
and context (class names, function names, or code) available:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/processing/merge.py
# def merge_weight_internal(symbols, DateTime, wind_code):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# tic()
# path = os.path.join(__CONFIG_H5_STK_WEIGHT_DIR__, wind_code)
# df = load_index_weight(path)
# print("数据加载完成")
# # 与行业不同，行业是全部有数据，它是有一部分有数据，所以直接用fillna会出错，需要先填充
# df.fillna(-1, inplace=True)
# toc()
#
# # 原始数据比较简单，但与行业板块数据又不一样
# # 1.每年的约定时间会调整成份股
# # 2.每天的值都不一样
# # 约定nan表示不属于成份，0表示属于成份，但权重为0
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # -1表示特殊数据，处理下
# df.replace(-1, np.nan, inplace=True)
# print("数据加载完成")
# toc()
#
# return df
#
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_dtype_remove_head(path, data, dtype, dataset_name):
# """
# 每个单元格的数据类型都一样
# 强行指定类型可以让文件的占用更小
# 表头不保存
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
# if dtype is None:
# f.create_dataset(dataset_name, data=data.as_matrix(), compression="gzip", compression_opts=6)
# else:
# f.create_dataset(dataset_name, data=data, compression="gzip", compression_opts=6, dtype=dtype)
# f.close()
# return
. Output only the next line. | DateTime = get_datetime(path) |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
分为两种
1. 全市场数据，将部分标记上权重
2. 只对历史上成为成份股的，进行处理，由于前面已经转换了数据，这里只要跳选数据并处理即可
以前的做法是先生成数据，然后再生成合约
"""
if __name__ == '__main__':
# 时间和合约都已经生成了
# 只要将时间与合约对上即可
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'Symbol.csv')
<|code_end|>
using the current file's imports:
import os
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.processing.merge import merge_weight_internal
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import write_dataframe_set_dtype_remove_head
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/processing/merge.py
# def merge_weight_internal(symbols, DateTime, wind_code):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# tic()
# path = os.path.join(__CONFIG_H5_STK_WEIGHT_DIR__, wind_code)
# df = load_index_weight(path)
# print("数据加载完成")
# # 与行业不同，行业是全部有数据，它是有一部分有数据，所以直接用fillna会出错，需要先填充
# df.fillna(-1, inplace=True)
# toc()
#
# # 原始数据比较简单，但与行业板块数据又不一样
# # 1.每年的约定时间会调整成份股
# # 2.每天的值都不一样
# # 约定nan表示不属于成份，0表示属于成份，但权重为0
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # -1表示特殊数据，处理下
# df.replace(-1, np.nan, inplace=True)
# print("数据加载完成")
# toc()
#
# return df
#
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_dtype_remove_head(path, data, dtype, dataset_name):
# """
# 每个单元格的数据类型都一样
# 强行指定类型可以让文件的占用更小
# 表头不保存
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
# if dtype is None:
# f.create_dataset(dataset_name, data=data.as_matrix(), compression="gzip", compression_opts=6)
# else:
# f.create_dataset(dataset_name, data=data, compression="gzip", compression_opts=6, dtype=dtype)
# f.close()
# return
. Output only the next line. | symbols = all_instruments(path) |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
分为两种
1. 全市场数据，将部分标记上权重
2. 只对历史上成为成份股的，进行处理，由于前面已经转换了数据，这里只要跳选数据并处理即可
以前的做法是先生成数据，然后再生成合约
"""
if __name__ == '__main__':
# 时间和合约都已经生成了
# 只要将时间与合约对上即可
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'Symbol.csv')
symbols = all_instruments(path)
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'DateTime.csv')
DateTime = get_datetime(path)
df = merge_weight_internal(symbols, DateTime, "000300.SH")
path = os.path.join(__CONFIG_H5_STK_DIR__, "5min_000300.SH", 'weight.h5')
<|code_end|>
. Use current file imports:
import os
from kquant_data.config import __CONFIG_H5_STK_DIR__
from kquant_data.processing.merge import merge_weight_internal
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import write_dataframe_set_dtype_remove_head
and context (classes, functions, or code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# Path: kquant_data/processing/merge.py
# def merge_weight_internal(symbols, DateTime, wind_code):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# tic()
# path = os.path.join(__CONFIG_H5_STK_WEIGHT_DIR__, wind_code)
# df = load_index_weight(path)
# print("数据加载完成")
# # 与行业不同，行业是全部有数据，它是有一部分有数据，所以直接用fillna会出错，需要先填充
# df.fillna(-1, inplace=True)
# toc()
#
# # 原始数据比较简单，但与行业板块数据又不一样
# # 1.每年的约定时间会调整成份股
# # 2.每天的值都不一样
# # 约定nan表示不属于成份，0表示属于成份，但权重为0
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # -1表示特殊数据，处理下
# df.replace(-1, np.nan, inplace=True)
# print("数据加载完成")
# toc()
#
# return df
#
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def write_dataframe_set_dtype_remove_head(path, data, dtype, dataset_name):
# """
# 每个单元格的数据类型都一样
# 强行指定类型可以让文件的占用更小
# 表头不保存
# :param path:
# :param data:
# :param dtype:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'w')
# if dtype is None:
# f.create_dataset(dataset_name, data=data.as_matrix(), compression="gzip", compression_opts=6)
# else:
# f.create_dataset(dataset_name, data=data, compression="gzip", compression_opts=6, dtype=dtype)
# f.close()
# return
. Output only the next line. | write_dataframe_set_dtype_remove_head(path, df, None, "weight") |
Next line prediction: <|code_start|> def __init__(self, folder):
self.prefix = 'tmp'
self.folder = folder
self.datetime = None
self.instruments = None
self.instruments_group = None
self.fields = None
self.group_len = 300
# datetime与bar_size是相关联的
self.bar_size = 86400
self.init_datetime()
self.init_symbols()
self.init_fields()
def init_datetime(self):
path = os.path.join(self.folder, 'DateTime.csv')
self.datetime.to_csv(path)
def init_symbols(self):
# 不再从导出列表中取，而是从文件夹中推算
path = os.path.join(self.folder, 'sh')
df_sh = get_symbols_from_path(path, "SSE")
path = os.path.join(self.folder, 'sz')
df_sz = get_symbols_from_path(path, "SZSE")
df = pd.concat([df_sh, df_sz])
self.instruments = df
path = os.path.join(self.folder, 'Symbol.csv')
self.instruments.to_csv(path, index=False)
<|code_end|>
. Use current file imports:
(import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
. Output only the next line. | self.instruments_group = split_into_group(self.instruments, self.group_len) |
Predict the next line after this snippet: <|code_start|> df = pd.concat([df_sh, df_sz])
self.instruments = df
path = os.path.join(self.folder, 'Symbol.csv')
self.instruments.to_csv(path, index=False)
self.instruments_group = split_into_group(self.instruments, self.group_len)
def init_fields(self):
pass
def read_data(self, market, code, bar_size):
return None
def _save_data(self, folder, raw_data, field):
data = raw_data.astype(np.float64).as_matrix()
path = os.path.join(folder, field + '.h5')
file = h5py.File(path, 'w')
file.create_dataset(field, data=data, compression="gzip", compression_opts=6)
file.close()
return None
def _merge_data(self, datetime, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
df = self.read_data(t['market'], t['code'], self.bar_size)
if df is None:
return None
del df['DateTime']
df = pd.merge(df, datetime, left_index=True, right_index=True, how='right', copy=False)
<|code_end|>
using the current file's imports:
import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path
and any relevant context from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
. Output only the next line. | df = filter_dataframe(df, None, None, None, self.fields) |
Predict the next line for this snippet: <|code_start|> pool = multiprocessing.Pool(processes=pool_size)
func = partial(self._merge_data, datetime, instruments)
pool_outputs = pool.map(func, range(len(instruments)))
else:
pool_outputs = []
for i in range(len(instruments)):
x = self._merge_data(datetime, instruments, i)
#if x is not None:
pool_outputs.append(x)
print("数据已经全部读取完成")
toc()
print("回收一下内存:%d" % gc.collect())
# 其中可能有Nono的，需要处理成nan，不能丢弃，否则可能导致Symbol.csv对不上
# pool_outputs
pool_outputs = pd.Panel(pool_outputs) # 内存不够，可能崩溃
pool_outputs = pool_outputs.transpose(1, 2, 0)
print("数据转置完成")
toc()
for i in range(len(self.fields)):
print(self.fields[i])
self._save_data(folder, pool_outputs.loc[i, :, :], self.fields[i])
toc()
print("回收一下内存:%d" % gc.collect())
def merge(self):
# 数据处理
<|code_end|>
with the help of current file imports:
import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path
and context from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
, which may contain function names, class names, or code. Output only the next line. | tic() |
Predict the next line after this snippet: <|code_start|> file.close()
return None
def _merge_data(self, datetime, instruments, i):
t = instruments.iloc[i]
print("%d %s" % (i, t['local_symbol']))
df = self.read_data(t['market'], t['code'], self.bar_size)
if df is None:
return None
del df['DateTime']
df = pd.merge(df, datetime, left_index=True, right_index=True, how='right', copy=False)
df = filter_dataframe(df, None, None, None, self.fields)
return tuple(df.T.values)
def _merge_branch(self, folder, datetime, instruments):
multi = False
if multi:
pool_size = multiprocessing.cpu_count() - 1
pool = multiprocessing.Pool(processes=pool_size)
func = partial(self._merge_data, datetime, instruments)
pool_outputs = pool.map(func, range(len(instruments)))
else:
pool_outputs = []
for i in range(len(instruments)):
x = self._merge_data(datetime, instruments, i)
#if x is not None:
pool_outputs.append(x)
print("数据已经全部读取完成")
<|code_end|>
using the current file's imports:
import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path
and any relevant context from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
. Output only the next line. | toc() |
Here is a snippet: <|code_start|> self._save_data(folder, pool_outputs.loc[i, :, :], self.fields[i])
toc()
print("回收一下内存:%d" % gc.collect())
def merge(self):
# 数据处理
tic()
# 想法对合约进行分组，分组后在对应目录下创建小文件，最后将小文件合并
for index, item in enumerate(self.instruments_group):
# 先创建目录
sub_folder = os.path.join(self.folder, "%s_%d" % (self.prefix, index))
os.makedirs(sub_folder, exist_ok=True)
self._merge_branch(sub_folder, self.datetime, item)
print("分批生成数据完成")
toc()
def hmerge(self):
# 合并数据
for i in range(len(self.fields)):
field = self.fields[i]
print(self.fields[i])
data = None
for index, item in enumerate(self.instruments_group):
# 先创建目录
sub_folder = os.path.join(self.folder, "%s_%d" % (self.prefix, index))
sub_file = os.path.join(sub_folder, "%s.h5" % field)
<|code_end|>
. Write the next line using the current file imports:
import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path
and context from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
, which may include functions, classes, or code. Output only the next line. | df = read_h5(sub_file, field) |
Continue the code snippet: <|code_start|>"""
每天获取数据，将数据合并成几个大的数据表
先取交易日，再取合约列表，然后全加载分成几个大表
"""
class MergeBar(object):
def __init__(self, folder):
self.prefix = 'tmp'
self.folder = folder
self.datetime = None
self.instruments = None
self.instruments_group = None
self.fields = None
self.group_len = 300
# datetime与bar_size是相关联的
self.bar_size = 86400
self.init_datetime()
self.init_symbols()
self.init_fields()
def init_datetime(self):
path = os.path.join(self.folder, 'DateTime.csv')
self.datetime.to_csv(path)
def init_symbols(self):
# 不再从导出列表中取，而是从文件夹中推算
path = os.path.join(self.folder, 'sh')
<|code_end|>
. Use current file imports:
import gc
import multiprocessing
import os
import shutil
import h5py
import numpy as np
import pandas as pd
from functools import partial
from .utils import split_into_group, filter_dataframe
from ..utils.xdatetime import tic, toc
from ..xio.h5 import read_h5
from ..stock.symbol import get_symbols_from_path
and context (classes, functions, or code) from other files:
# Path: kquant_data/processing/utils.py
# def split_into_group(arr, n):
# out = [arr[i:i + n] for i in range(0, len(arr), n)]
# return out
#
# def filter_dataframe(df, index_name=None, start_date=None, end_date=None, fields=None):
# if index_name is not None:
# df['index_datetime'] = df[index_name].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('index_datetime')
# # 过滤时间
# if start_date is not None or end_date is not None:
# df = df[start_date:end_date]
# # 过滤字段
# if fields is not None:
# df = df[fields]
# return df
#
# Path: kquant_data/utils/xdatetime.py
# def tic():
# """
# 对应MATLAB中的tic
# :return:
# """
# globals()['tt'] = time.clock()
#
# def toc():
# """
# 对应MATLAB中的toc
# :return:
# """
# t = time.clock() - globals()['tt']
# print('\nElapsed time: %.8f seconds\n' % t)
# return t
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/stock/symbol.py
# def get_symbols_from_path(path, exchange):
# """
# 指定目录，将目录转成合约列表
# :param path:
# :param exchange:
# :return:
# """
# list1 = []
# list2 = []
# list3 = []
# list4 = []
# list5 = []
# for dirpath, dirnames, filenames in os.walk(path):
# for filename in filenames:
# list1.append(filename[:8])
# list2.append(filename[:2])
# list3.append(filename[2:8])
# list4.append("%s.%s" % (filename[2:8], exchange))
# list5.append("%s.%s" % (filename[2:8], filename[:2].upper()))
#
# df = pd.DataFrame({"local_symbol": list1, "market": list2, "code": list3, "symbol": list4, "wind_code": list5})
#
# return df
. Output only the next line. | df_sh = get_symbols_from_path(path, "SSE") |
Given the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载版块相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__)
# 移动到下一个交易日
date_str = (datetime.today() + timedelta(days=0)).strftime('%Y-%m-%d')
new_trading_days = trading_days[date_str:]
date_str = (new_trading_days.iloc[1, 0]).strftime('%Y-%m-%d')
new_trading_days = trading_days['1999-01-04':date_str]
<|code_end|>
, generate the next line using the imports in this file:
import sys
from datetime import datetime, timedelta
from WindPy import w
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__, __CONFIG_TDAYS_SHFE_FILE__
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SHFE_FILE__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'tdays', 'SHFE.csv')
#
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
. Output only the next line. | download_sector(w, new_trading_days, sector_name="大商所全部品种", root_path=__CONFIG_H5_FUT_SECTOR_DIR__) |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载版块相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
<|code_end|>
. Write the next line using the current file imports:
import sys
from datetime import datetime, timedelta
from WindPy import w
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__, __CONFIG_TDAYS_SHFE_FILE__
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SHFE_FILE__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'tdays', 'SHFE.csv')
#
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
, which may include functions, classes, or code. Output only the next line. | trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载版块相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
<|code_end|>
. Use current file imports:
(import sys
from datetime import datetime, timedelta
from WindPy import w
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__, __CONFIG_TDAYS_SHFE_FILE__
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SHFE_FILE__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'tdays', 'SHFE.csv')
#
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
. Output only the next line. | trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__) |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载版块相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__)
# 移动到下一个交易日
date_str = (datetime.today() + timedelta(days=0)).strftime('%Y-%m-%d')
new_trading_days = trading_days[date_str:]
date_str = (new_trading_days.iloc[1, 0]).strftime('%Y-%m-%d')
new_trading_days = trading_days['1999-01-04':date_str]
<|code_end|>
using the current file's imports:
import sys
from datetime import datetime, timedelta
from WindPy import w
from kquant_data.config import __CONFIG_H5_FUT_SECTOR_DIR__, __CONFIG_TDAYS_SHFE_FILE__
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_SECTOR_DIR__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SHFE_FILE__ = os.path.join(__CONFIG_H5_FUT_DIR__, 'tdays', 'SHFE.csv')
#
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
. Output only the next line. | download_sector(w, new_trading_days, sector_name="大商所全部品种", root_path=__CONFIG_H5_FUT_SECTOR_DIR__) |
Given snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
rule = '1day'
if False:
sector_name = '风险警示股票'
<|code_end|>
, continue by predicting the next line. Consider current file imports:
import sys
from kquant_data.processing.merge import merge_sector, merge_sectors
and context:
# Path: kquant_data/processing/merge.py
# def merge_sector(rule, sector_name, dataset_name):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'Symbol.csv')
# symbols = all_instruments(path)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'DateTime.csv')
# DateTime = get_datetime(path)
#
# tic()
# path = os.path.join(__CONFIG_H5_STK_SECTOR_DIR__, sector_name)
# df = load_sector(path, 1)
# print("数据加载完成")
# toc()
#
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # 有些股票从来没有被ST过，比如浦发银行，或一些新股
# df.fillna(0, inplace=True)
#
# print("数据加载完成")
# toc()
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, "%s.h5" % dataset_name)
# write_dataframe_set_dtype_remove_head(path, df, np.int8, dataset_name)
#
# toc()
#
# def merge_sectors(rule, sector_name, dataset_name):
# """
# 合并二级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'Symbol.csv')
# symbols = all_instruments(path)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'DateTime.csv')
# DateTime = get_datetime(path)
#
# tic()
# path = os.path.join(__CONFIG_H5_STK_SECTOR_DIR__, sector_name)
# df = load_sectors(path)
# print("数据加载完成")
# toc()
#
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # df.to_csv(r"D:\1.csv")
# # 有些股票从来没有被ST过，比如浦发银行，或一些新股
# df.fillna(0, inplace=True)
#
# print("数据加载完成")
# toc()
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, "%s.h5" % dataset_name)
# write_dataframe_set_dtype_remove_head(path, df, np.int16, dataset_name)
#
# toc()
which might include code, classes, or functions. Output only the next line. | merge_sector(rule, sector_name, 'ST') |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
指定数据目录，生成对应的合约行业数据
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
rule = '1day'
if False:
sector_name = '风险警示股票'
merge_sector(rule, sector_name, 'ST')
if True:
sector_name = '中信证券一级行业指数'
<|code_end|>
. Use current file imports:
import sys
from kquant_data.processing.merge import merge_sector, merge_sectors
and context (classes, functions, or code) from other files:
# Path: kquant_data/processing/merge.py
# def merge_sector(rule, sector_name, dataset_name):
# """
# 合并一级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'Symbol.csv')
# symbols = all_instruments(path)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'DateTime.csv')
# DateTime = get_datetime(path)
#
# tic()
# path = os.path.join(__CONFIG_H5_STK_SECTOR_DIR__, sector_name)
# df = load_sector(path, 1)
# print("数据加载完成")
# toc()
#
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # 有些股票从来没有被ST过，比如浦发银行，或一些新股
# df.fillna(0, inplace=True)
#
# print("数据加载完成")
# toc()
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, "%s.h5" % dataset_name)
# write_dataframe_set_dtype_remove_head(path, df, np.int8, dataset_name)
#
# toc()
#
# def merge_sectors(rule, sector_name, dataset_name):
# """
# 合并二级文件夹
# :param rule:
# :param sector_name:
# :param dataset_name:
# :return:
# """
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'Symbol.csv')
# symbols = all_instruments(path)
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, 'DateTime.csv')
# DateTime = get_datetime(path)
#
# tic()
# path = os.path.join(__CONFIG_H5_STK_SECTOR_DIR__, sector_name)
# df = load_sectors(path)
# print("数据加载完成")
# toc()
#
# df = expand_dataframe_according_to(df, DateTime.index, symbols['wind_code'])
# # df.to_csv(r"D:\1.csv")
# # 有些股票从来没有被ST过，比如浦发银行，或一些新股
# df.fillna(0, inplace=True)
#
# print("数据加载完成")
# toc()
#
# path = os.path.join(__CONFIG_H5_STK_DIR__, rule, "%s.h5" % dataset_name)
# write_dataframe_set_dtype_remove_head(path, df, np.int16, dataset_name)
#
# toc()
. Output only the next line. | merge_sectors(rule, sector_name, 'Sector') |
Here is a snippet: <|code_start|># -*- coding: utf-8 -*-
"""
期货的处理方法
"""
def bar_size_2_folder(bar_size):
ret = {
86400: '86400_DEF1_MC1',
3600: '3600_DEF1_MC1_1530_EXT',
}[bar_size]
return ret
def get_relative_path(market, code, bar_size):
# D:\DATA_FUT_HDF5\Data_Processed\86400_DEF1_MC1\a.h5
folder = bar_size_2_folder(bar_size)
file_ext = 'h5'
filename = "%s.%s" % (code, file_ext)
return os.path.join(folder, filename)
def get_absolute_path(root_dir, market, code, bar_size):
return os.path.join(root_dir, get_relative_path(market, code, bar_size))
def read_future(market, code, bar_size, path):
if path is None:
<|code_end|>
. Write the next line using the current file imports:
import os
import pandas as pd
from ..config import __CONFIG_H5_FUT_MARKET_DATA_DIR__
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_MARKET_DATA_DIR__ = r'D:\DATA_FUT_HDF5\Data_P2'
, which may include functions, classes, or code. Output only the next line. | _path = get_absolute_path(__CONFIG_H5_FUT_MARKET_DATA_DIR__, market, code, bar_size) |
Based on the snippet: <|code_start|>
#
columns = ['DateTime', 'Open', 'High', 'Low', 'Close', 'Amount', 'Volume', 'na']
tmp = pd.DataFrame(data[-10:], columns=columns)
# 要是没有成交额就惨了
r = tmp.Amount / tmp.Volume / tmp.Close
# 为了解决价格扩大了多少倍的问题
type_unit = np.power(10, np.round(np.log10(r))).median()
data = list(map(int_to_float, data, [type_unit] * len(data)))
with open(output_file, 'wb') as f:
pack_records(formats_lc5, data, f)
if __name__ == '__main__':
# 先将数据转换格式，然后手工复制即可
# 以下代码可以复制两次，sh与sz分别处理即可
input_path = r'D:\test\\5'
output_path = r'D:\test\\lc5'
for dirpath, dirnames, filenames in os.walk(input_path, topdown=True):
for filename in filenames:
shotname, extension = os.path.splitext(filename)
if extension != '.5':
continue
input_filname = os.path.join(input_path, filename)
ouput_filname = os.path.join(output_path, '%s.lc5' % shotname)
min_5_to_lc5(input_filname, ouput_filname)
print(ouput_filname)
if True:
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import numpy as np
import pandas as pd
import struct
from ctypes import create_string_buffer
from kquant_data.stock.tdx import read_file
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/stock/tdx.py
# def read_file(path, instrument_type='stock'):
# """
# http://www.tdx.com.cn/list_66_68.html
# 通达信本地目录有day/lc1/lc5三种后缀名，两种格式
# 从通达信官网下载的5分钟后缀只有5这种格式，为了处理方便，时间精度都只到分钟
# :param path:
# :return:
# """
# columns = ['DateTime', 'Open', 'High', 'Low', 'Close', 'Amount', 'Volume', 'na']
#
# file_ext = os.path.splitext(path)[1][1:]
# if instrument_type == 'stock':
# ohlc_type = {'day': 'i4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
# formats = ['i4'] + [ohlc_type] * 4 + ['f4'] + ['i4'] * 2
# elif instrument_type == 'option':
# ohlc_type = {'day': 'f4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
# formats = ['i4'] + [ohlc_type] * 4 + ['i4'] + ['i4'] * 2
# date_parser = {'day': day_datetime_long,
# '5': min_datetime_long,
# 'lc1': min_datetime_long,
# 'lc5': min_datetime_long,
# }[file_ext]
#
# dtype = np.dtype({'names': columns, 'formats': formats})
# data = np.fromfile(path, dtype=dtype)
# df = pd.DataFrame(data)
# # 为了处理的方便，存一套long类型的时间
# df['DateTime'] = df['DateTime'].apply(date_parser)
# df['datetime'] = df['DateTime'].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('datetime')
# df = df.drop('na', 1)
#
# # 有两种格式的数据的价格需要调整
# if instrument_type == 'stock':
# if file_ext == 'day' or file_ext == '5':
# tmp = df.tail(10)
# r = tmp.Amount / tmp.Volume / tmp.Close
# # 为了解决价格扩大了多少倍的问题
# type_unit = np.power(10, np.round(np.log10(r))).median()
# # 这个地方要考虑到实际情况，不要漏价格，也不要把时间做了除法
# df.ix[:, 1:5] = df.ix[:, 1:5] * type_unit
#
# # 转换格式，占用内存更少
# df['DateTime'] = df['DateTime'].astype(np.uint64)
# df['Open'] = df['Open'].astype(np.float32)
# df['High'] = df['High'].astype(np.float32)
# df['Low'] = df['Low'].astype(np.float32)
# df['Close'] = df['Close'].astype(np.float32)
# df['Amount'] = df['Amount'].astype(np.float32)
# df['Volume'] = df['Volume'].astype(np.uint32)
#
# # print(df.dtypes)
#
# return df
. Output only the next line. | df_5 = read_file(input_filname) |
Given the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
"""
def resume_download_tdays(w, enddate, path):
"""
增量下载
:return:
"""
<|code_end|>
, generate the next line using the imports in this file:
import pandas as pd
from ..wind.tdays import read_tdays, download_tdays, write_tdays
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# def download_tdays(w, startdate, enddate, option=''):
# """
# 下载交易日数据
# :param w:
# :param startdate:
# :param enddate:
# :param option:
# :return:
# """
# w.asDateTime = asDateTime
# w_tdays_data = w.tdays(startdate, enddate, option)
# df = pd.DataFrame(w_tdays_data.Data, )
# df = df.T
# df.columns = ['date']
# df['date'] = pd.to_datetime(df['date'])
#
# return df
#
# def write_tdays(path, df):
# df.to_csv(path, date_format='%Y-%m-%d', encoding='utf-8', index=False)
. Output only the next line. | df_old = read_tdays(path) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
"""
def resume_download_tdays(w, enddate, path):
"""
增量下载
:return:
"""
df_old = read_tdays(path)
if df_old is None:
startdate = '1991-01-01'
else:
startdate = df_old.index[-1]
<|code_end|>
. Use current file imports:
(import pandas as pd
from ..wind.tdays import read_tdays, download_tdays, write_tdays)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# def download_tdays(w, startdate, enddate, option=''):
# """
# 下载交易日数据
# :param w:
# :param startdate:
# :param enddate:
# :param option:
# :return:
# """
# w.asDateTime = asDateTime
# w_tdays_data = w.tdays(startdate, enddate, option)
# df = pd.DataFrame(w_tdays_data.Data, )
# df = df.T
# df.columns = ['date']
# df['date'] = pd.to_datetime(df['date'])
#
# return df
#
# def write_tdays(path, df):
# df.to_csv(path, date_format='%Y-%m-%d', encoding='utf-8', index=False)
. Output only the next line. | df_new = download_tdays(w, startdate, enddate, option="") |
Based on the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
"""
def resume_download_tdays(w, enddate, path):
"""
增量下载
:return:
"""
df_old = read_tdays(path)
if df_old is None:
startdate = '1991-01-01'
else:
startdate = df_old.index[-1]
df_new = download_tdays(w, startdate, enddate, option="")
df = pd.concat([df_old, df_new])
# 可能要‘去重’，也可能None不能参与合并
<|code_end|>
, predict the immediate next line with the help of imports:
import pandas as pd
from ..wind.tdays import read_tdays, download_tdays, write_tdays
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# def download_tdays(w, startdate, enddate, option=''):
# """
# 下载交易日数据
# :param w:
# :param startdate:
# :param enddate:
# :param option:
# :return:
# """
# w.asDateTime = asDateTime
# w_tdays_data = w.tdays(startdate, enddate, option)
# df = pd.DataFrame(w_tdays_data.Data, )
# df = df.T
# df.columns = ['date']
# df['date'] = pd.to_datetime(df['date'])
#
# return df
#
# def write_tdays(path, df):
# df.to_csv(path, date_format='%Y-%m-%d', encoding='utf-8', index=False)
. Output only the next line. | write_tdays(path, df) |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
期货的处理方法
"""
def read_option(market, code, bar_size, path):
if path is None:
# _path = get_absolute_path(__CONFIG_H5_FUT_MARKET_DATA_DIR__, market, code, bar_size)
pass
else:
file_ext = 'day'
filename = "8#%s.%s" % (code, file_ext)
_path = os.path.join(path, filename)
<|code_end|>
with the help of current file imports:
import os
import pandas as pd
from ..config import __CONFIG_H5_FUT_MARKET_DATA_DIR__
from ..stock.tdx import read_file
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_FUT_MARKET_DATA_DIR__ = r'D:\DATA_FUT_HDF5\Data_P2'
#
# Path: kquant_data/stock/tdx.py
# def read_file(path, instrument_type='stock'):
# """
# http://www.tdx.com.cn/list_66_68.html
# 通达信本地目录有day/lc1/lc5三种后缀名，两种格式
# 从通达信官网下载的5分钟后缀只有5这种格式，为了处理方便，时间精度都只到分钟
# :param path:
# :return:
# """
# columns = ['DateTime', 'Open', 'High', 'Low', 'Close', 'Amount', 'Volume', 'na']
#
# file_ext = os.path.splitext(path)[1][1:]
# if instrument_type == 'stock':
# ohlc_type = {'day': 'i4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
# formats = ['i4'] + [ohlc_type] * 4 + ['f4'] + ['i4'] * 2
# elif instrument_type == 'option':
# ohlc_type = {'day': 'f4', '5': 'i4', 'lc1': 'f4', 'lc5': 'f4'}[file_ext]
# formats = ['i4'] + [ohlc_type] * 4 + ['i4'] + ['i4'] * 2
# date_parser = {'day': day_datetime_long,
# '5': min_datetime_long,
# 'lc1': min_datetime_long,
# 'lc5': min_datetime_long,
# }[file_ext]
#
# dtype = np.dtype({'names': columns, 'formats': formats})
# data = np.fromfile(path, dtype=dtype)
# df = pd.DataFrame(data)
# # 为了处理的方便，存一套long类型的时间
# df['DateTime'] = df['DateTime'].apply(date_parser)
# df['datetime'] = df['DateTime'].apply(yyyyMMddHHmm_2_datetime)
# df = df.set_index('datetime')
# df = df.drop('na', 1)
#
# # 有两种格式的数据的价格需要调整
# if instrument_type == 'stock':
# if file_ext == 'day' or file_ext == '5':
# tmp = df.tail(10)
# r = tmp.Amount / tmp.Volume / tmp.Close
# # 为了解决价格扩大了多少倍的问题
# type_unit = np.power(10, np.round(np.log10(r))).median()
# # 这个地方要考虑到实际情况，不要漏价格，也不要把时间做了除法
# df.ix[:, 1:5] = df.ix[:, 1:5] * type_unit
#
# # 转换格式，占用内存更少
# df['DateTime'] = df['DateTime'].astype(np.uint64)
# df['Open'] = df['Open'].astype(np.float32)
# df['High'] = df['High'].astype(np.float32)
# df['Low'] = df['Low'].astype(np.float32)
# df['Close'] = df['Close'].astype(np.float32)
# df['Amount'] = df['Amount'].astype(np.float32)
# df['Volume'] = df['Volume'].astype(np.uint32)
#
# # print(df.dtypes)
#
# return df
, which may contain function names, class names, or code. Output only the next line. | df = read_file(_path, 'option') |
Based on the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载行业分类相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
date_str = datetime.today().strftime('%Y-%m-%d')
<|code_end|>
, predict the immediate next line with the help of imports:
import sys
from WindPy import w
from datetime import datetime
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector, download_sectors
from kquant_data.config import __CONFIG_H5_STK_SECTOR_DIR__, __CONFIG_TDAYS_SSE_FILE__
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# def download_sectors(
# w,
# trading_days,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 指定行业列表后，下载其中的数据，带子目录
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# # 下载板块数据
# path = os.path.join(root_path, '%s.csv' % sector_name)
# sectors = pd.read_csv(path, encoding='utf-8-sig')
#
# for i in range(0, len(sectors)):
# print(sectors.iloc[i, :])
#
# wind_code = sectors.ix[i, 'wind_code']
# sec_name = sectors.ix[i, 'sec_name']
#
# foldpath = os.path.join(root_path, sector_name, sec_name)
# try:
# os.mkdir(foldpath)
# except:
# pass
#
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=None, windcode=wind_code, field='wind_code', is_indexconstituent=False)
# # 移除多余的数据文件
# dst_path = os.path.join(root_path, "%s_move" % sector_name, sec_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
. Output only the next line. | trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__) |
Next line prediction: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载行业分类相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
date_str = datetime.today().strftime('%Y-%m-%d')
trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__)
trading_days = trading_days['2004-06-01':date_str]
# 按频率来看数据是稀疏的，但需要每天下载一次
if True:
download_sectors(w, trading_days, sector_name="中信证券一级行业指数", root_path=__CONFIG_H5_STK_SECTOR_DIR__)
if False:
<|code_end|>
. Use current file imports:
(import sys
from WindPy import w
from datetime import datetime
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector, download_sectors
from kquant_data.config import __CONFIG_H5_STK_SECTOR_DIR__, __CONFIG_TDAYS_SSE_FILE__)
and context including class names, function names, or small code snippets from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# def download_sectors(
# w,
# trading_days,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 指定行业列表后，下载其中的数据，带子目录
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# # 下载板块数据
# path = os.path.join(root_path, '%s.csv' % sector_name)
# sectors = pd.read_csv(path, encoding='utf-8-sig')
#
# for i in range(0, len(sectors)):
# print(sectors.iloc[i, :])
#
# wind_code = sectors.ix[i, 'wind_code']
# sec_name = sectors.ix[i, 'sec_name']
#
# foldpath = os.path.join(root_path, sector_name, sec_name)
# try:
# os.mkdir(foldpath)
# except:
# pass
#
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=None, windcode=wind_code, field='wind_code', is_indexconstituent=False)
# # 移除多余的数据文件
# dst_path = os.path.join(root_path, "%s_move" % sector_name, sec_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
. Output only the next line. | download_sector(w, trading_days, sector_name="风险警示股票", root_path=__CONFIG_H5_STK_SECTOR_DIR__) |
Given the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载行业分类相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
date_str = datetime.today().strftime('%Y-%m-%d')
trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__)
trading_days = trading_days['2004-06-01':date_str]
# 按频率来看数据是稀疏的，但需要每天下载一次
if True:
<|code_end|>
, generate the next line using the imports in this file:
import sys
from WindPy import w
from datetime import datetime
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector, download_sectors
from kquant_data.config import __CONFIG_H5_STK_SECTOR_DIR__, __CONFIG_TDAYS_SSE_FILE__
and context (functions, classes, or occasionally code) from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# def download_sectors(
# w,
# trading_days,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 指定行业列表后，下载其中的数据，带子目录
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# # 下载板块数据
# path = os.path.join(root_path, '%s.csv' % sector_name)
# sectors = pd.read_csv(path, encoding='utf-8-sig')
#
# for i in range(0, len(sectors)):
# print(sectors.iloc[i, :])
#
# wind_code = sectors.ix[i, 'wind_code']
# sec_name = sectors.ix[i, 'sec_name']
#
# foldpath = os.path.join(root_path, sector_name, sec_name)
# try:
# os.mkdir(foldpath)
# except:
# pass
#
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=None, windcode=wind_code, field='wind_code', is_indexconstituent=False)
# # 移除多余的数据文件
# dst_path = os.path.join(root_path, "%s_move" % sector_name, sec_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
. Output only the next line. | download_sectors(w, trading_days, sector_name="中信证券一级行业指数", root_path=__CONFIG_H5_STK_SECTOR_DIR__) |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载行业分类相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
date_str = datetime.today().strftime('%Y-%m-%d')
trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__)
trading_days = trading_days['2004-06-01':date_str]
# 按频率来看数据是稀疏的，但需要每天下载一次
if True:
<|code_end|>
. Write the next line using the current file imports:
import sys
from WindPy import w
from datetime import datetime
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector, download_sectors
from kquant_data.config import __CONFIG_H5_STK_SECTOR_DIR__, __CONFIG_TDAYS_SSE_FILE__
and context from other files:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# def download_sectors(
# w,
# trading_days,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 指定行业列表后，下载其中的数据，带子目录
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# # 下载板块数据
# path = os.path.join(root_path, '%s.csv' % sector_name)
# sectors = pd.read_csv(path, encoding='utf-8-sig')
#
# for i in range(0, len(sectors)):
# print(sectors.iloc[i, :])
#
# wind_code = sectors.ix[i, 'wind_code']
# sec_name = sectors.ix[i, 'sec_name']
#
# foldpath = os.path.join(root_path, sector_name, sec_name)
# try:
# os.mkdir(foldpath)
# except:
# pass
#
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=None, windcode=wind_code, field='wind_code', is_indexconstituent=False)
# # 移除多余的数据文件
# dst_path = os.path.join(root_path, "%s_move" % sector_name, sec_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
, which may include functions, classes, or code. Output only the next line. | download_sectors(w, trading_days, sector_name="中信证券一级行业指数", root_path=__CONFIG_H5_STK_SECTOR_DIR__) |
Using the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
下载行业分类相关信息
"""
# 解决Python 3.6的pandas不支持中文路径的问题
print(sys.getfilesystemencoding()) # 查看修改前的
try:
sys._enablelegacywindowsfsencoding() # 修改
print(sys.getfilesystemencoding()) # 查看修改后的
except:
pass
if __name__ == '__main__':
w.start()
date_str = datetime.today().strftime('%Y-%m-%d')
<|code_end|>
, determine the next line of code. You have imports:
import sys
from WindPy import w
from datetime import datetime
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wset import download_sector, download_sectors
from kquant_data.config import __CONFIG_H5_STK_SECTOR_DIR__, __CONFIG_TDAYS_SSE_FILE__
and context (class names, function names, or code) available:
# Path: kquant_data/wind/tdays.py
# def read_tdays(path):
# try:
# df = pd.read_csv(path, parse_dates=True)
# except:
# return None
#
# df['date'] = pd.to_datetime(df['date'])
# df.index = df['date']
# return df
#
# Path: kquant_data/wind_resume/wset.py
# def download_sector(
# w,
# trading_days,
# root_path,
# sector_name="风险警示股票"):
# """
# 下载ST股票的信息，在已有的文件中补数据，这种不会多下载
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
#
# foldpath = os.path.join(root_path, sector_name)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=sector_name, windcode=None, field='wind_code', is_indexconstituent=False)
#
# dst_path = os.path.join(root_path, "%s_move" % sector_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# def download_sectors(
# w,
# trading_days,
# root_path,
# sector_name="中信证券一级行业指数"):
# """
# 指定行业列表后，下载其中的数据，带子目录
# :param w:
# :param trading_days:
# :param sector_name:
# :param root_path:
# :return:
# """
# # 下载板块数据
# path = os.path.join(root_path, '%s.csv' % sector_name)
# sectors = pd.read_csv(path, encoding='utf-8-sig')
#
# for i in range(0, len(sectors)):
# print(sectors.iloc[i, :])
#
# wind_code = sectors.ix[i, 'wind_code']
# sec_name = sectors.ix[i, 'sec_name']
#
# foldpath = os.path.join(root_path, sector_name, sec_name)
# try:
# os.mkdir(foldpath)
# except:
# pass
#
# df = trading_days
# df['date_str'] = trading_days['date'].astype(str)
# file_download_constituent(w, df['date_str'], foldpath, '.csv',
# sector=None, windcode=wind_code, field='wind_code', is_indexconstituent=False)
# # 移除多余的数据文件
# dst_path = os.path.join(root_path, "%s_move" % sector_name, sec_name)
# if not os.path.exists(dst_path):
# os.makedirs(dst_path)
# move_constituent(foldpath, dst_path)
#
# Path: kquant_data/config.py
# __CONFIG_H5_STK_SECTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'sectorconstituent')
#
# __CONFIG_TDAYS_SSE_FILE__ = os.path.join(__CONFIG_H5_STK_DIR__, 'tdays', 'SSE.csv')
. Output only the next line. | trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__) |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
读取数据示例
"""
"""
基础数据准备
"""
start_date = '2007' # 测试开始时间
end_date = None # 结束时间
input_path = r'D:\DATA_STK\daily' # 指定输入数据目录
output_path = 'tmp_data' # 指定输入数据目录
path = os.path.join(input_path, 'DateTime.csv')
<|code_end|>
. Use current file imports:
import os
import numpy as np
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import read_h5
from kquant_data.processing.utils import ndarray_to_dataframe
and context (classes, functions, or code) from other files:
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/processing/utils.py
# def ndarray_to_dataframe(arr, index, columns, start=None, end=None):
# df = pd.DataFrame(arr, index=index, columns=columns)
# df = df[start:end]
# return df
. Output only the next line. | DateTime = get_datetime(path) |
Based on the snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
读取数据示例
"""
"""
基础数据准备
"""
start_date = '2007' # 测试开始时间
end_date = None # 结束时间
input_path = r'D:\DATA_STK\daily' # 指定输入数据目录
output_path = 'tmp_data' # 指定输入数据目录
path = os.path.join(input_path, 'DateTime.csv')
DateTime = get_datetime(path)
path = os.path.join(input_path, 'Symbol.csv')
<|code_end|>
, predict the immediate next line with the help of imports:
import os
import numpy as np
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import read_h5
from kquant_data.processing.utils import ndarray_to_dataframe
and context (classes, functions, sometimes code) from other files:
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/processing/utils.py
# def ndarray_to_dataframe(arr, index, columns, start=None, end=None):
# df = pd.DataFrame(arr, index=index, columns=columns)
# df = df[start:end]
# return df
. Output only the next line. | df_Symbols = all_instruments(path) |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
读取数据示例
"""
"""
基础数据准备
"""
start_date = '2007' # 测试开始时间
end_date = None # 结束时间
input_path = r'D:\DATA_STK\daily' # 指定输入数据目录
output_path = 'tmp_data' # 指定输入数据目录
path = os.path.join(input_path, 'DateTime.csv')
DateTime = get_datetime(path)
path = os.path.join(input_path, 'Symbol.csv')
df_Symbols = all_instruments(path)
Symbols = df_Symbols['wind_code']
"""
行情数据准备
"""
# 一定要复权，但需要选择好复权的时机
path = os.path.join(input_path, 'Close.h5')
<|code_end|>
using the current file's imports:
import os
import numpy as np
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import read_h5
from kquant_data.processing.utils import ndarray_to_dataframe
and any relevant context from other files:
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/processing/utils.py
# def ndarray_to_dataframe(arr, index, columns, start=None, end=None):
# df = pd.DataFrame(arr, index=index, columns=columns)
# df = df[start:end]
# return df
. Output only the next line. | Close = read_h5(path, 'Close') |
Here is a snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
读取数据示例
"""
"""
基础数据准备
"""
start_date = '2007' # 测试开始时间
end_date = None # 结束时间
input_path = r'D:\DATA_STK\daily' # 指定输入数据目录
output_path = 'tmp_data' # 指定输入数据目录
path = os.path.join(input_path, 'DateTime.csv')
DateTime = get_datetime(path)
path = os.path.join(input_path, 'Symbol.csv')
df_Symbols = all_instruments(path)
Symbols = df_Symbols['wind_code']
"""
行情数据准备
"""
# 一定要复权，但需要选择好复权的时机
path = os.path.join(input_path, 'Close.h5')
Close = read_h5(path, 'Close')
<|code_end|>
. Write the next line using the current file imports:
import os
import numpy as np
from kquant_data.api import get_datetime, all_instruments
from kquant_data.xio.h5 import read_h5
from kquant_data.processing.utils import ndarray_to_dataframe
and context from other files:
# Path: kquant_data/api.py
# def get_datetime(path):
# dt = pd.read_csv(path, index_col=0, parse_dates=True)
# dt['date'] = dt.index
# return dt
#
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
#
# Path: kquant_data/xio/h5.py
# def read_h5(path, dateset_name):
# """
# 将简单数据读取出来
# 返回的东西有头表，就是DataFrame，没表头就是array
# :param path:
# :param dateset_name:
# :return:
# """
# f = h5py.File(path, 'r')
#
# d = f[dateset_name][:]
#
# f.close()
# return d
#
# Path: kquant_data/processing/utils.py
# def ndarray_to_dataframe(arr, index, columns, start=None, end=None):
# df = pd.DataFrame(arr, index=index, columns=columns)
# df = df[start:end]
# return df
, which may include functions, classes, or code. Output only the next line. | Close = ndarray_to_dataframe(Close, DateTime.index, columns=Symbols, start=start_date, end=end_date) |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
if __name__ == '__main__':
w.start()
# 加载股票列表，这里需要在每天收盘后导出日线数据才能做
<|code_end|>
. Use current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
from kquant_data.wind_resume.wsd import resume_download_delist_date
from kquant_data.api import all_instruments
and context (classes, functions, or code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_delist_date(
# w,
# wind_codes,
# root_path,
# field='delist_date',
# dtype=np.datetime64):
# """
# 下载每支股票的delist_date
# 如果以后有同类的每个股票一个数，但可能上新股票都得更新的field就可以用
# :param w:
# :param wind_codes:
# :param field:
# :param dtype:
# :param root_path:
# :return:
# """
# wind_codes_set = set(wind_codes)
#
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# path = os.path.join(root_path, '%s.csv' % field)
# if dtype == np.datetime64:
# df_old = read_datetime_dataframe(path)
# else:
# df_old = read_data_dataframe(path)
#
# if df_old is None:
# new_symbols = wind_codes_set
# else:
# df_old.dropna(axis=1, inplace=True)
# new_symbols = wind_codes_set - set(df_old.columns)
#
# # 没有新数据好办，只有一个数据怎么办？会出错吗
# if len(new_symbols) == 0:
# print('没有空合约，没有必要更新%s' % field)
# # 可能排序不行，还是再处理下
# df_new = df_old.copy()
# else:
# # 第一次下全，以后每次下最新的
# df_new = download_daily_at(w, list(new_symbols), field, date_str)
#
# # 新旧数据的合并
# df = pd.DataFrame(columns=wind_codes)
# if df_old is not None:
# df[df_old.columns] = df_old
# df.index = df_new.index
# df[df_new.columns] = df_new
# else:
# df = df_new
#
# # 排序有点乱，得处理
# df = df[wind_codes]
# if dtype == np.datetime64:
# write_datetime_dataframe(path, df)
# else:
# write_data_dataframe(path, df)
#
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
. Output only the next line. | path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv') |
Continue the code snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
if __name__ == '__main__':
w.start()
# 加载股票列表，这里需要在每天收盘后导出日线数据才能做
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
Symbols = all_instruments(path)
wind_codes = Symbols['wind_code']
# 增量下载ipo_date，由于每周都有上市，但因为新上市股票不参加交易，所以看情况进行
if True:
<|code_end|>
. Use current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
from kquant_data.wind_resume.wsd import resume_download_delist_date
from kquant_data.api import all_instruments
and context (classes, functions, or code) from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_delist_date(
# w,
# wind_codes,
# root_path,
# field='delist_date',
# dtype=np.datetime64):
# """
# 下载每支股票的delist_date
# 如果以后有同类的每个股票一个数，但可能上新股票都得更新的field就可以用
# :param w:
# :param wind_codes:
# :param field:
# :param dtype:
# :param root_path:
# :return:
# """
# wind_codes_set = set(wind_codes)
#
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# path = os.path.join(root_path, '%s.csv' % field)
# if dtype == np.datetime64:
# df_old = read_datetime_dataframe(path)
# else:
# df_old = read_data_dataframe(path)
#
# if df_old is None:
# new_symbols = wind_codes_set
# else:
# df_old.dropna(axis=1, inplace=True)
# new_symbols = wind_codes_set - set(df_old.columns)
#
# # 没有新数据好办，只有一个数据怎么办？会出错吗
# if len(new_symbols) == 0:
# print('没有空合约，没有必要更新%s' % field)
# # 可能排序不行，还是再处理下
# df_new = df_old.copy()
# else:
# # 第一次下全，以后每次下最新的
# df_new = download_daily_at(w, list(new_symbols), field, date_str)
#
# # 新旧数据的合并
# df = pd.DataFrame(columns=wind_codes)
# if df_old is not None:
# df[df_old.columns] = df_old
# df.index = df_new.index
# df[df_new.columns] = df_new
# else:
# df = df_new
#
# # 排序有点乱，得处理
# df = df[wind_codes]
# if dtype == np.datetime64:
# write_datetime_dataframe(path, df)
# else:
# write_data_dataframe(path, df)
#
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
. Output only the next line. | resume_download_delist_date(w, wind_codes, __CONFIG_H5_STK_FACTOR_DIR__, |
Predict the next line after this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
if __name__ == '__main__':
w.start()
# 加载股票列表，这里需要在每天收盘后导出日线数据才能做
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
Symbols = all_instruments(path)
wind_codes = Symbols['wind_code']
# 增量下载ipo_date，由于每周都有上市，但因为新上市股票不参加交易，所以看情况进行
if True:
<|code_end|>
using the current file's imports:
import os
import numpy as np
from WindPy import w
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
from kquant_data.wind_resume.wsd import resume_download_delist_date
from kquant_data.api import all_instruments
and any relevant context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_delist_date(
# w,
# wind_codes,
# root_path,
# field='delist_date',
# dtype=np.datetime64):
# """
# 下载每支股票的delist_date
# 如果以后有同类的每个股票一个数，但可能上新股票都得更新的field就可以用
# :param w:
# :param wind_codes:
# :param field:
# :param dtype:
# :param root_path:
# :return:
# """
# wind_codes_set = set(wind_codes)
#
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# path = os.path.join(root_path, '%s.csv' % field)
# if dtype == np.datetime64:
# df_old = read_datetime_dataframe(path)
# else:
# df_old = read_data_dataframe(path)
#
# if df_old is None:
# new_symbols = wind_codes_set
# else:
# df_old.dropna(axis=1, inplace=True)
# new_symbols = wind_codes_set - set(df_old.columns)
#
# # 没有新数据好办，只有一个数据怎么办？会出错吗
# if len(new_symbols) == 0:
# print('没有空合约，没有必要更新%s' % field)
# # 可能排序不行，还是再处理下
# df_new = df_old.copy()
# else:
# # 第一次下全，以后每次下最新的
# df_new = download_daily_at(w, list(new_symbols), field, date_str)
#
# # 新旧数据的合并
# df = pd.DataFrame(columns=wind_codes)
# if df_old is not None:
# df[df_old.columns] = df_old
# df.index = df_new.index
# df[df_new.columns] = df_new
# else:
# df = df_new
#
# # 排序有点乱，得处理
# df = df[wind_codes]
# if dtype == np.datetime64:
# write_datetime_dataframe(path, df)
# else:
# write_data_dataframe(path, df)
#
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
. Output only the next line. | resume_download_delist_date(w, wind_codes, __CONFIG_H5_STK_FACTOR_DIR__, |
Predict the next line for this snippet: <|code_start|>#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
执行次数很早的算法
比如下载行业分类列表，下载
"""
if __name__ == '__main__':
w.start()
# 加载股票列表，这里需要在每天收盘后导出日线数据才能做
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
<|code_end|>
with the help of current file imports:
import os
import numpy as np
from WindPy import w
from kquant_data.config import __CONFIG_H5_STK_DIR__, __CONFIG_H5_STK_FACTOR_DIR__
from kquant_data.wind_resume.wsd import resume_download_delist_date
from kquant_data.api import all_instruments
and context from other files:
# Path: kquant_data/config.py
# __CONFIG_H5_STK_DIR__ = r'D:\DATA_STK'
#
# __CONFIG_H5_STK_FACTOR_DIR__ = os.path.join(__CONFIG_H5_STK_DIR__, 'factor')
#
# Path: kquant_data/wind_resume/wsd.py
# def resume_download_delist_date(
# w,
# wind_codes,
# root_path,
# field='delist_date',
# dtype=np.datetime64):
# """
# 下载每支股票的delist_date
# 如果以后有同类的每个股票一个数，但可能上新股票都得更新的field就可以用
# :param w:
# :param wind_codes:
# :param field:
# :param dtype:
# :param root_path:
# :return:
# """
# wind_codes_set = set(wind_codes)
#
# date_str = datetime.today().strftime('%Y-%m-%d')
#
# path = os.path.join(root_path, '%s.csv' % field)
# if dtype == np.datetime64:
# df_old = read_datetime_dataframe(path)
# else:
# df_old = read_data_dataframe(path)
#
# if df_old is None:
# new_symbols = wind_codes_set
# else:
# df_old.dropna(axis=1, inplace=True)
# new_symbols = wind_codes_set - set(df_old.columns)
#
# # 没有新数据好办，只有一个数据怎么办？会出错吗
# if len(new_symbols) == 0:
# print('没有空合约，没有必要更新%s' % field)
# # 可能排序不行，还是再处理下
# df_new = df_old.copy()
# else:
# # 第一次下全，以后每次下最新的
# df_new = download_daily_at(w, list(new_symbols), field, date_str)
#
# # 新旧数据的合并
# df = pd.DataFrame(columns=wind_codes)
# if df_old is not None:
# df[df_old.columns] = df_old
# df.index = df_new.index
# df[df_new.columns] = df_new
# else:
# df = df_new
#
# # 排序有点乱，得处理
# df = df[wind_codes]
# if dtype == np.datetime64:
# write_datetime_dataframe(path, df)
# else:
# write_data_dataframe(path, df)
#
# Path: kquant_data/api.py
# def all_instruments(path=None, type=None):
# """
# 得到合约列表
# :param type:
# :return:
# """
# if path is None:
# path = os.path.join(__CONFIG_H5_STK_DIR__, "daily", 'Symbol.csv')
#
# df = pd.read_csv(path, dtype={'code': str})
#
# return df
, which may contain function names, class names, or code. Output only the next line. | Symbols = all_instruments(path) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.